All Activity

The announcement drove a large increase in Better's stock price, but UWM, Rocket and Pennymac all saw any gains earlier in the day more than dissipate. View the full article

Three leading Middle East economies consider options as US-Israeli campaign against Tehran continues View the full article

Fintech in AI search plays by much stricter rules. Because it’s a Your Money or Your Life category, products must clear higher verification thresholds before AI mentions you: Is your product legitimate? Are your fees and protections explicit? Do other trusted sources back up your claims? To find these answers, AI draws from your website and the wider web — including sources you don’t control. That risks misrepresentation of your brand. What matters, then, is whether those sources tell an accurate story. In this article, I’ll explain how to influence that narrative. Because the real goal is for your fintech brand to show up in AI search AND be represented accurately. 3 Types of AI Visibility in Fintech Your fintech brand can appear in AI search in three ways. Your goal is to show up in all three. Mentioned when AI explains topics in your category Cited and linked within the answers Recommended as part of a shortlist of products Brand Mentions Brand mentions are when AI systems include your name in an answer. They’re great for brand awareness. These references put your name in front of buyers even when they’re not seeking you out. For example, I asked ChatGPT: “Are buy now, pay later providers ideal for my business?” It mentioned several BNPL platforms in its response. This suggests the AI recognizes those brands as part of the category and relevant in the space. You’ll often see mentions as: Lists embedded inside explanations: “Popular BNPL providers include X, Y, Z…” Examples supporting a point: “Some neobanks, like X and Y, offer…” Context for user stories: “Many users switch from traditional banks to apps like X…” A mention isn’t a recommendation, but it still matters. Mentions often appear in non-brand queries. That’s when users begin exploring their options. And if they see your brand mentioned often, it builds familiarity. (Also known as the mere exposure effect.) So, by the time a user reaches the decision stage, they’re more likely to recognize your name. But sometimes that isn’t enough. That’s why having a positive brand sentiment is vital. The way AI mentions your brand can shape buyer perception. If it often frames your product as “known for strong security,” that idea sticks. But if the AI always pairs your name with warnings like “high fees” or “frequent outages,” it can raise doubts. Citations Citations are when the AI uses your pages to support its answer. They’re valuable for boosting credibility and consumer trust. When AI uses your content as a source, there’s an implied endorsement. It references you because you’re trustworthy. And when you’re consistently cited, your brand becomes associated with expertise in the topic. Citations may appear differently across platforms and prompts. Sometimes they appear as footnotes and/or as inline links. You might also see a sidebar or expandable panel with grouped sources. Other times, citations appear as thumbnails somewhere in the AI’s response. Regardless of format, the principle is the same: When the AI cites your documentation, it signals that your content is being treated as a reliable source. It’s pulling information from your pages to build its answer. And this allows you to influence how the AI explains your product. For example, I asked ChatGPT: “What reporting and analytics does Klarna offer brands after implementation?” Many of the citations came from Klarna’s documentation. This implies that Klarna has some level of influence over the AI’s answer. There’s a caveat with citations, however. LLMs might link to your site, but that doesn’t always mean more traffic. Citations are less visible than brand mentions or recommendations. I rarely click them myself. If I need more detail, I’ll usually continue the conversation within the platform. Or switch to Google search. That’s likely true for many users. Still, citations signal that AI systems trust your documentation. And that trust enables product recommendations, which we’ll cover next. Further reading: E-E-A-T in the AI Era: Complete Guide + Free Audit Product Recommendations Product recommendations are when AI includes your brand or product in a shortlist. They’re the most impactful type of AI visibility because they influence which brands users consider. And ultimately, which product they choose. Here’s what recommendations look like in ChatGPT and Google AI Mode. I asked: “Which BNPL platform is good for mid-size ecommerce brands?” Both listed Klarna as one of the top options. This places Klarna front and center as buyers narrow their options. Showing up in high-intent queries is vital for recommendations. These are prompts that include “top,” “best,” “compare,” or “alternative.” Such as: What are good alternatives to X for a small business What are the best budgeting apps for freelancers List the best neobanks with high-yield savings But showing up in these queries isn’t automatic. AI systems use specific signals to decide which brands to recommend. How LLMs Choose Which Fintech Brands to Feature AI acts as a filter between buyers and brands. So how do these systems decide which brands and products to recommend? From what we can observe, comes down to two signals: consensus and consistency. Consensus Consensus is when multiple reputable sources mention your brand and product. AI surfaces brands that have this kind of social proof — it suggests that you’re real, trustworthy, and worth recommending. The stronger the consensus, the more confident AI is in featuring you. But this cuts both ways. If sources consistently highlight negatives, AI may repeat those warnings instead. In fintech, AI systems likely assess consensus from several sources, including: Partner-bank and infrastructure disclosures Regulatory databases Personal finance publishers Finance communities and review platforms Partner sites Technical and investor communities So, a big part of AI optimization is showing up in the sources LLMs use to form consensus. The easiest way to identify those sources is to run brand-related prompts. Example: “Best banks for international transfers.” Then, check which sites appear in the citations. Those are the sources the AI model trusts. When these sites and reviews talk about your brand, it increases your chances of being mentioned by AI. Further reading: Read our search everywhere optimization guide for tips on building a positive brand reputation across platforms. Consistency It’s not enough for your brand to be mentioned everywhere. The sources also need to agree on the facts they’re sharing about you. That means the core details of your product align all over the internet, including your: Category Pricing and fees Product features Protections For example, I asked ChatGPT and Google AI Mode for “best budgeting apps.” Both recommended YNAB (You Need A Budget). That’s no surprise. YNAB appears in dozens of reputable sources, including Money, CNBC, NerdWallet, and Wirecutter. It’s also frequently mentioned in finance communities, such as myFICO Forum. These sources also highlight specific use cases: college students, goal-setting, and overall budgeting. These consistency signals help AI confidently recommend YNAB for those exact scenarios. Building consistency across platforms comes down to good ole PR and reputation management. Ensure your key details align across your site and third-party coverage. Working with publishers and affiliates will help you shape how your brand is described. Ultimately, consistency starts with content: what you publish and what others publish about you. 3 Types of Content That Dominate Fintech in AI Search LLMs will reference any public content they can access. In fintech, three types carry the most weight. 1. Owned Content Owned content is anything you publish and control on your own properties. This includes your website, documentation, and any branded platforms. AI analyzes these places for your version of the facts. That’s why content like “What does this product do?” or “How does it work?” is so essential. For example, I asked ChatGPT: “Compare ATM withdrawal limits, card spending caps, and international FX fees for Wise, Revolut, and Monzo.” Its answer cited many of the three brands’ pricing and product pages to build the comparison. This indicates the AI uses these pages to answer this query. For you, this means your website plays a big role in what AI says about your product. Treat your site as both a marketing and educational channel. Publish the product details that matter to buyers. Look at your sales conversations, support tickets, and comparison research to identify questions, concerns, and pain points. For example, Intuit’s TurboTax landing page includes extensive product details. It covers everything from security and guarantees to key tax filing information. This helps the AI (and users) understand what the product includes, how it works, and who it’s for. 2. Earned Media and Reviews Earned media and reviews are third-party perspectives on your product. This includes everything from editorial coverage to user feedback. LLMs use these sources to fact-check your claims. It’s also how they understand what it’s like to use your product. In fintech, third-party sources often include: Editorial guides and roundups by established finance sites such as Kiplinger and MarketWatch Affiliate and review platforms, including sites like the Better Business Bureau (BBB) Community discussions on platforms such as Quora and finance forums like MoneySavingExpert For example, I asked ChatGPT: “What reporting and analytics does Klarna offer brands after implementation?” The citations included Klarna’s own documentation. Plus, sites such as print-on-demand platform Gelato, Forbes, and G2. That mix is worth noting. It shows the AI isn’t taking Klarna’s claims at face value. It’s cross-checking them against third-party evaluations. The takeaway here is to treat reputable third-party coverage as a core growth channel. One proven strategy: publish original research that journalists can cite. Take KPMG’s Pulse of Fintech H1 2025 Report, for example. Each edition generates media coverage across major sites like Bloomberg and Trinetix. This works because reporters are constantly hunting for newsworthy statistics. Other things you can do to increase earned mentions include: Fill out or update third-party listings you control, like app store profiles Co-author articles to earn mentions in trusted sources Synchronize PR, product, legal, and marketing so your brand story stays unified everywhere Further reading: LLM Seeding: A New SEO Strategy to Get Mentioned by LLMs 3. Official Records Official records are documents that confirm your legal authorization to operate. LLMs treat them as proof and confirmation of compliance and regulatory standing. The types of official records LLMs cite include: Regulatory registries and licenses Regulatory disclosures and notices Partner bank disclosures Corporate records These sources allow the AI to answer questions on legitimacy and protection. For example, I asked Perplexity: “Is Wise licensed to operate in the U.S., and what protections apply to Wise balances?” The citations included: A PDF consent order from six state financial regulators Wise’s National Trust application filed with the OCC The California DFPI’s regulated-entity page for Wise US, Inc. Along with Wise’s documentation, these give AI enough evidence to answer confidently. They confirmed that Wise: Operates in the U.S. Under specific entities And with the appropriate approvals and protections In fintech AI search, this kind of regulatory confirmation is a strong trust signal. It tells AI systems that your product is legitimate and safe to mention. This creates a real opportunity for you. AI systems can only cite what they can find, parse, and verify. Your job is to make your regulatory standing explicit, structured, and easy to retrieve. Start by naming your partner banks, custodians, and key infrastructure providers on your site. And keep those details up to date across your site. Publish key pages that AI systems can pull from, including: Regulatory and licensing: Clearly list your licenses, registration numbers, regulatory bodies, and jurisdictions where you operate Protection: Explain in plain language how funds are safeguarded, what insurance applies, and which entities custody assets Link to these pages from your footer and trust pages so AI bots can easily find them. How Fintech Brands Can Improve AI Search Visibility and Accuracy 54% of Americans now turn to ChatGPT for financial research, according to a Motley Fool Money study. That means buyers often get the “AI version” of your brand before they see your website. That’s actually good news. A Microsoft study found that AI traffic converts at 3x the rate of other channels. This includes search, direct, and social media. The catch? This only works in your favor if the model accurately describes your product. Here’s how to help it do that. Provide Proof That Your Brand Is Real and Trustworthy LLMs need proof they can validate before they include you in answers. So your trust details need to be public and clear across your owned platforms. One effective way to do this is with a dedicated section on your site. This can serve as your primary source of truth. Many fintech brands, like SoFi, do this with a “Trust & Security Center.” But a well-structured “Help Center” like Venmo’s works, too: Overall, make it easy for LLMs (and users) to find the facts that reduce perceived risk: Who holds the funds Who powers the product How the product works Reiterate the same trust details in related pages and sections of your site. Add them to your homepage, About page, and FAQ sections on service pages. Many fintech brands also include disclosures, like Member FDIC or partner bank language, in the footer. A keyword tool like Semrush’s Keyword Magic can help you find safety and trust concerns people have about your company. If they’re asking these questions on Google, you can bet they’re asking them in AI tools, too. How you format your content is crucial. Ensure it’s easy for AI to extract and cite: Use question-and-answer structures for common concerns Answer each question with a clear, direct, and quotable response Include facts and statistics when applicable Finally, treat data hygiene as a required part of your process. When a partner, protection, or operational flow changes, update the documentation immediately. Then clean up anything outdated. Redirect or remove old PDFs and help docs so AI only finds the current version. Further reading: Content Writing 101: How to Create High Quality Content Reduce Mixed Messages About Your Product Online Contradictions undermine AI’s trust in your brand. They break the consistency signal, making AI systems cautious about recommending you. But inconsistencies can easily happen over time. As your company evolves, public-facing information can become outdated. Older pages, screenshots, or explanations remain discoverable online. But AI systems can’t always determine which version is current. The good news is that you can fix this with a few focused actions. First, start where you have complete control: your own site. Ensure your core narrative, product details, and trust documentation are fully synchronized on all landing pages and trust hubs. SoFi does this well. Their “all-in-one” app positioning is reinforced throughout their site. As you update your site, have marketing, product, and compliance teams work together. This ensures consistency in promotional materials, regulatory disclosures, and product specs. Next, make sure that affiliate and “best-of” publishers accurately describe you. Affiliate sites and finance publishers are the most-cited sources in AI answers, according to the Semrush AI Visibility Index (December 2025). So, it’s worth checking what these sites are currently saying about you. (Especially on “best of” listicles, comparisons, and reviews.) To do this, research the questions people ask when evaluating your product. They’re usually formatted like this: Is [Brand X] legit [Brand Y] fees Can I trust [Brand Z] [Brand X] vs [Brand Y] Is [Brand X] safe Google’s People Also Ask and keyword tools let you find these questions. You can also use Semrush’s AI Visibility Toolkit to see what questions users ask LLMs about your industry. It tells you the exact prompts they use: Then, look at which pages and websites are often cited. If you’re using the AI Visibility Toolkit, it will pull these for you: Otherwise, manually search the questions you found in different generative engines. Click each source and scan for inconsistencies. If you find something wrong, reach out to the publisher for a correction or update. Make it easy for them to make changes by providing clear, publish-ready facts. Another vital step is monitoring (and participating in) online finance conversations. Forum and social media posts have a long shelf life. This can pose consistency problems for you as your company grows and your products change. Reddit, for example, rarely deletes old posts. So outdated answers can stay discoverable for years. Reduce the impact of outdated information by: Replying with a simple correction, especially in threads you see cited in AI engines Making sure your social media accounts repeat the correct version Announcing updates where people discuss your category It’s also worth being more present in communities that AI often cites. For example, Fidelity’s subreddit often shows up in Fidelity-related questions. If you manage or participate in spaces like this, you can influence the public record directly. Use our brand subreddit guide for tips on setting one up and growing your visibility. Manage Brand Perception and Sentiment AI systems assess how other sites talk about you. That public sentiment shapes the answers users get. For example, I asked ChatGPT: “Is PayPal safe?” It didn’t give a definitive “yes.” Instead, it used qualifying language like “generally considered” and “not perfect.” It also added important caveats and security considerations. Looking at the citations, you can see the sources that contributed to those caveats: Investopedia, comparing PayPal’s safety measures to credit cards Community discussions, such as r/privacy, where users debate PayPal’s risk profile Editorial sites and even some competitors like Wise outlining protections and limitations This means: How other sites describe you affects how AI describes you. That makes sentiment tracking vital. Set up regular AI search visibility audits for your brand. You can do this manually by monitoring different AI platforms. Start with the top two most used generative AI tools: ChatGPT and Google AI Overviews. Each month, run a consistent set of high-intent prompts related to your brand and category. Note the sentiment, including any differences between AI models. Look for patterns to assess whether your sentiment is positive, neutral, or negative: Regular positive framing Repeated warnings Recurring pros and cons Yes, doing this manually takes time. If you’d rather automate the process, use the AI Visibility Toolkit. For example, it provides your brand’s overall sentiment and share of voice. It breaks this down by platforms, including: Google AI Mode ChatGPT Perplexity Gemini You can also see how you stack up against your biggest fintech competitors. The Narrative Drivers tool is especially useful. It shows the exact questions people ask about your brand. And the percentage of favorable sentiment towards you in each answer. This makes it easy to see where you’re perceived positively or negatively at scale. Really cool. Make Your Fintech Brand Easy for AI to Trust AI is changing fintech in a number of ways. Most notably, a buyer’s first touchpoint is now often an AI-generated answer. If you’re not in those answers, you’re not in the decision. The fix: build consistency and consensus signals for your fintech brand. You already have a strong idea of how to do that. Now, dive deeper into the topic with our AI optimization guide. The post Fintech in AI Search: How to Be the Trusted & Featured Brand appeared first on Backlinko. View the full article

Don’t bring your mom or dad to an interview with Shark Tank investor Kevin O’Leary if you were planning on it. On a recent appearance on Fox Business’ Varney & Co., O’Leary argued that doing so—bringing parents to a job interview—sends a “horrific signal” to employers, and calls it a “big red flag.” “First question I’d have to the son or daughter, I’d say, ‘Do you want me to hire your mother or you? What’s she doing here?'” O’Leary said. “That résumé goes right into the garbage.” This isn’t simply a hypothetical situation. The data shows a not insignificant number of young jobseekers are tapping in parents throughout the hiring process to boost their odds—even if it ends up backfiring against them. In a survey conducted by Zety and Pollfish of roughly 1,000 Gen Z workers, 20% said a parent has joined them during a job interview. That included 15% who said this happened in person, and 5% virtually. One in five Gen Z workers said a parent has also reached out to an employer or recruiter on their behalf, according to Zety. Over 40% of Gen Z respondents said their parents helped them draft their résumé. O’Leary has experienced this firsthand. “It happened to me on a Zoom call, and I just said, this isn’t going to work,” he told Fox Business. “Your mom is not gonna be part of this discussion,” he said. This is an alarm bell, he said. “I want to find out if you can think independently, make decisions independently,” O’Leary said. “It just shows you that this person doesn’t have the confidence or ability to do the mandate that you’re offering them.” The entrepreneur also has some advice for other business leaders who might find themselves in a similar situation. “Just say: ‘Sorry. That’s not going to work for us,’” he said. “It means you can’t do this on your own. I think it’s a horrific signal—and I really think that parents that are overbearing like this think that they’re going to add value.” Millennials will recall facing similar accusations in their early careers. Besides the Zety survey, there’s additional data that suggests the number of Gen Z tapping in their parents for job interviews could be high, too. According to a 2025 study by Resume Templates, 77% of surveyed Gen Z job seekers have brought a parent along to a job interview. Some have even gotten them to negotiate pay raises and complete hiring tests on their behalf. But this isn’t solely a case of mollycoddling parents: Only 41% of young people said they were “highly confident” navigating the job market, according to a 2025 report from Big Brothers Big Sisters of America and The Harris Poll. This is largely to do with a lack of professional mentorship as well as a highly-competitive job market that means each interview comes with increased pressure. Despite O’Leary’s warnings, however, most Gen Z workers are competent, independent workers who can ace an interview without the help of mom and dad: 80% of respondents to Zety’s survey said their parents had no involvement during interviews. Adding to that, over half of Gen Z respondents said they would feel “embarrassed or upset” if their parents reached out to their employer without their knowledge. View the full article

Many homeowners and first-time buyers are surprised by rising property taxes and insurance, which can sharply increase monthly mortgage costs beyond principal and interest. View the full article

We may earn a commission from links on this page. Deal pricing and availability subject to change after time of publication. The Samsung HW-C450 soundbar is one of the best ultra-thin soundbars on the market, and at less than two inches thick, it packs some serious power with six drivers and built-in amplification. A compact, minimalist option for small rooms (or just for those who don’t want a bulky, unsightly audio accessory ruining their media-viewing room), this highly rated 2.1-channel soundbar with a wireless subwoofer just dropped 29% on Amazon, bringing its price to $139.97 (originally $197.99). Samsung HW-C450 Soundbar $139.97 at Amazon $197.99 Save $58.02 Get Deal Get Deal $139.97 at Amazon $197.99 Save $58.02 While it doesn’t have the surround sound of a 5.1 system, CNET notes that this soundbar delivers “impressive sound quality” despite its small size, with better-than-average sound detail for movies and TV shows. While it features modes like Game Mode and Bass Boost, if your primary goal is to use it for music, you may want to look elsewhere. The HW-C450 also lacks HDMI connectivity compared to models with multiple inputs, like the Sony HT-CT150. The display features illuminated, touch-sensitive buttons if you don’t want to use the remote (or tend to misplace it), and there’s no need for a wire to connect the included sub, which provides additional low-end punch. It’s a sleek and powerful option for a home theater experience, making it a great option for decor-minded buyers looking to beef up their TV and movie-watching experience with style. The slim design fits neatly under TVs without obscuring the screen, and at under $150, now is a great time to invest in the surprisingly bassy Samsung HW-C450 soundbar if you’re on a budget. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods 4 Active Noise Cancelling Wireless Earbuds — $119.00 (List Price $179.00) Samsung Galaxy S26, Unlocked Android Smartphone + $100 Gift Card, 512GB, Powerful Processor, Galaxy AI, Immersive Viewing, Durable Battery, 2026, Black — $899.99 (List Price $1,199.99) Samsung Galaxy Buds 4 AI Noise Cancelling Wireless Earbuds + $20 Amazon Gift Card — $179.99 (List Price $199.99) Google Pixel 10a 128GB 6.3" Unlocked Smartphone + $100 Gift Card — $499.00 (List Price $599.00) Apple iPad 11" 128GB A16 WiFi Tablet (Blue, 2025) — $329.00 (List Price $349.00) Apple Watch Series 11 [GPS 46mm] Smartwatch with Jet Black Aluminum Case with Black Sport Band - M/L. Sleep Score, Fitness Tracker, Health Monitoring, Always-On Display, Water Resistant — $329.00 (List Price $429.00) Amazon Fire TV Soundbar — $99.99 (List Price $119.99) Deals are selected by our commerce team View the full article

A payroll firm is a specialized service provider that handles various aspects of payroll processing for businesses. These firms manage wage calculations, tax withholdings, and compliance with labor regulations, which can greatly reduce the burden on your HR team. By outsourcing payroll, you can save time and minimize errors, allowing your organization to operate more efficiently. Comprehending how a payroll firm functions and the specific benefits it offers can be essential for your business’s success. Key Takeaways A payroll firm manages payroll processes, including wage calculations, tax withholdings, and compliance, ensuring accuracy and efficiency. Outsourcing payroll saves time for HR teams, allowing them to focus on core business functions and enhancing overall productivity. Payroll firms help businesses navigate complex tax laws, ensuring compliance and reducing the risk of penalties and errors. Advanced technology and automation in payroll services minimize manual calculations, saving significant time and improving accuracy. Payroll firms offer scalable solutions with predictable costs, making it easier for businesses to budget and manage payroll expenses. What Is a Payroll Firm? A payroll firm is an external service provider that takes on the responsibility of managing payroll processes for businesses. These firms specialize in calculating wages, handling tax withholdings, and ensuring compliance with payroll regulations. By utilizing advanced software, a Paychex firm can automate payroll processing, considerably reducing the time spent on calculations and diminishing the risk of errors and compliance issues. For clinics, clinic payroll services are particularly beneficial, as they often include additional features like tax filing, year-end reporting, and employee self-service portals. Outsourcing payroll functions allows you to reclaim valuable time, as many business leaders spend over 11 hours weekly on HR administration. Furthermore, payroll firms help mitigate financial risks associated with tax penalties, ensuring that payments are made accurately and on time according to federal, state, and local regulations. This all-encompassing approach makes payroll firms an essential asset for any business. Key Responsibilities of a Payroll Firm When you partner with a payroll firm, you benefit from their key responsibilities, which include payroll processing automation, tax compliance management, and employee payment services. They streamline your payroll operations, ensuring that employee wages are calculated accurately and paid on time, whilst also handling complex tax regulations to keep your business compliant. Payroll Processing Automation Payroll processing automation streamlines the intricate tasks associated with managing employee wages and compliance. By automating wage calculations, payroll firms reduce processing time from over an hour bi-weekly to just three minutes. This efficiency minimizes errors, ensuring accurate tax withholding and wage garnishment, which helps you avoid penalties. Furthermore, payroll firms handle federal, state, and local tax filings, relieving you of the regulatory compliance burden. They integrate time tracking and attendance systems, enhancing the accuracy of wage calculations so employees are paid correctly for their hours worked. Many payroll service providers likewise offer self-service applications, allowing employees to access pay statements and update personal information, in the end improving employee experience and satisfaction within your business. Tax Compliance Management Effective tax compliance management is vital for businesses, as it guarantees that you accurately withhold federal, state, and local taxes, thereby reducing the risk of costly penalties. Payroll firms play an important role in this process by: Handling the filing of payroll tax returns, including quarterly and year-end submissions, which alleviates your administrative burden. Staying updated on regulatory changes to help you remain compliant with evolving tax laws. Managing complex compliance requirements, such as wage garnishments and unemployment insurance filings, with specialized expertise. Automating tax calculations and payments to save you time and minimize errors associated with manual payroll processes. Employee Payment Services Ensuring employees receive accurate and timely payments is a fundamental responsibility of any payroll firm. These firms calculate wages based on hours worked, overtime, and necessary deductions, guaranteeing that employees are paid correctly. They also manage tax withholdings and garnishments, ensuring compliance with federal, state, and local regulations to help you avoid financial penalties. Payroll firms automate the filing of payroll taxes and year-end reporting, allowing you to concentrate on your core operations. With direct deposit services, employees can receive paychecks electronically, enhancing convenience. Furthermore, many payroll firms offer self-service portals where employees can access pay statements, update personal information, and manage benefits, in the end improving the overall employee experience and engagement within your business. Benefits of Outsourcing Payroll When you choose to outsource payroll, you can greatly streamline your business operations. This decision not only saves you time but additionally improves accuracy and security. Here are four key benefits of outsourcing payroll: Time Savings: Payroll providers handle calculations, filings, and payments, allowing your HR team to focus on core business functions. Error Reduction: Automation minimizes errors in wage calculations and tax filings, reducing the risk of costly penalties. Improved Security: Specialized firms use encryption and secure platforms to protect sensitive employee information, ensuring data security. Expert Guidance: Access to payroll expertise helps you navigate complex regulations and provides timely support during audits and tax season. How Payroll Firms Ensure Compliance Payroll firms play a crucial role in helping businesses navigate the intricacies of compliance with various tax regulations. They stay updated on federal, state, and local tax laws, which guarantees you remain compliant and reduces the risk of costly penalties from misfiling or late submissions. By automating complex compliance processes, like filing 941 tax forms, payroll firms minimize errors and guarantee timely payments to tax authorities. Many providers even offer assurances on tax accuracy, covering the costs of any penalties incurred as a result of filing mistakes. Regular audits and compliance checks help identify and rectify discrepancies in payroll processing and tax obligations, guaranteeing adherence to labor laws. Furthermore, by managing wage garnishments and unemployment insurance claims, payroll firms help you navigate regulatory requirements effectively, reducing your overall financial risk. Relying on these experts allows you to focus on growing your business without the constant worry of compliance issues. Choosing the Right Payroll Firm for Your Business When you’re choosing a payroll firm, it’s vital to first identify your specific business needs to guarantee the firm can effectively address your challenges. Next, evaluate how well the provider integrates with your existing accounting software, as smooth data transfer is critical for efficient operations. Finally, assess the availability of their customer support, since timely assistance can help you navigate any payroll issues that arise. Identify Business Needs Identifying your business needs is crucial for selecting the right payroll firm, especially as various factors influence your requirements. Start by listing your current payroll challenges, such as compliance issues or time spent on calculations. Next, consider: The frequency of payroll processing you need. Whether your employees work in multiple states or countries, affecting tax regulations. Your potential for employee growth over the next year, which will require scalable solutions. The importance of integrations with time-tracking software and accounting systems to streamline payroll and improve accuracy. Evaluate Integration Capabilities Choosing the right payroll firm hinges on evaluating its integration capabilities, as seamless connections with your existing systems can greatly boost your operational efficiency. Make sure the payroll provider integrates smoothly with your accounting software, facilitating efficient data transfer and minimizing manual entry errors. A firm with human resources integration supports real-time updates for payroll-related policy changes, guaranteeing compliance. Check if the service includes time tracking and attendance integration, as this improves wage calculation accuracy and streamlines processes. Furthermore, look for benefits integration, allowing automatic deductions for health insurance and retirement contributions. Finally, assess the compatibility of self-service applications, as mobile-friendly access enables employees to check pay statements and update personal information easily, enhancing overall experience. Assess Support Availability How can you guarantee that your payroll needs are met without unnecessary delays? Evaluating support availability is essential when selecting a payroll firm. Here are some key factors to take into account: Support Hours: Verify they offer assistance during peak times, like year-end tax filing. Contact Methods: Look for firms that provide multiple channels, such as phone, email, and live chat, to improve accessibility. Dedicated Support: Analyze if they assign dedicated account managers or support teams for personalized service. User Feedback: Investigate reviews and testimonials to understand the responsiveness and effectiveness of their customer support. Common Challenges in Payroll Processing Payroll processing poses several common challenges that can substantially impact a business’s financial health and operational efficiency. One major issue is tax compliance, with over $65.5 billion in civil penalties reported in 2023 as a result of payroll errors. These mistakes can lead to unexpected tax burdens and audits, costing businesses billions annually. Furthermore, small business owners often spend 3 to 10 hours each month on payroll taxes, with some dedicating over 10 hours, which indicates a significant time investment that could be streamlined. The complexity of payroll increases with flexible employee schedules and changing regulations, making accurate management difficult. In addition, maintaining organized payroll information is fundamental for future reporting, as these figures play an important role in hiring projections, tax calculations, and compliance with labor laws. Addressing these challenges is necessary to guarantee efficient payroll processing and mitigate risks. The Role of Technology in Payroll Services As businesses endeavor for greater efficiency in their operations, the role of technology in payroll services has become increasingly vital. Implementing advanced payroll systems can streamline your payroll process, offering several key benefits: Cloud-Based Automation: Automate payroll processing to reduce manual calculations, potentially saving you over 90% of the time spent on payroll management. Mobile Access: Manage payroll and access employee information from any device, enhancing flexibility and allowing for remote operations. Integration: Seamlessly connect with accounting software and time-tracking systems to maintain accurate wage calculations and improve data flow. Self-Service Options: Empower employees to access pay statements and update personal information on their own, increasing engagement and satisfaction. Additionally, advanced systems often include automated compliance monitoring, helping you stay informed about regulatory changes and minimizing the risk of costly payroll errors. Cost Considerations for Payroll Services When evaluating payroll services, comprehending the costs involved is crucial for making informed decisions that align with your business needs. Typically, payroll services range from $30 to $100 per employee each month, depending on the complexity of the services and your business size. Most providers charge a monthly base fee plus a per-employee processing fee, which helps you budget more effectively. For instance, ADP offers customized packages designed to client needs, providing flexibility in pricing based on specific requirements. As in-house payroll may seem cheaper upfront, outsourcing delivers predictable fees and scalability, saving you time and reducing compliance risks. Moreover, consider any extra services offered by payroll providers; these may incur additional costs but can improve payroll efficiency and compliance. Transitioning to a New Payroll Provider Shifting to a new payroll provider can be a critical decision for your business, especially if your current system isn’t meeting your needs. You can change at any time, allowing for timely adjustments. To guarantee a smooth process, follow these steps: Prepare Payroll Data: Gather detailed employee and tax information for the new provider. Verify Tax History: Check for errors in your tax history to meet obligations and avoid penalties. Conduct Payroll Verification: Review bank, employee, and tax information thoroughly for accuracy before the first payroll run. Cancel Old Services: Only cancel your old payroll provider after the first successful payroll run with the new provider to prevent payment disruptions. Frequently Asked Questions What Is the Point of a Payroll Company? A payroll company simplifies the payroll process for you, automating wage calculations and handling tax withholdings. By ensuring compliance with various regulations, it helps prevent expensive penalties. You’ll save time and reduce administrative burdens, allowing your HR team to focus on more strategic tasks. Many payroll firms likewise offer guarantees on tax accuracy, providing you with peace of mind and minimizing the risk of audits, making payroll management more efficient and reliable. What Is Payroll and What Are Its Advantages? Payroll is the process of calculating and distributing employee wages, managing tax withholdings, and guaranteeing compliance with labor laws. Its advantages include saving you time, as it can reduce manual processing from over an hour to just three minutes. By using payroll services, you assure accurate tax filings and compliance, minimizing the risk of costly penalties. Furthermore, employee self-service portals improve the experience by allowing workers to access pay statements easily. How Do Payroll Companies Make Money? Payroll companies make money primarily through monthly base fees and per-employee processing fees, which can range from $30 to $100 depending on the services offered. They additionally generate additional revenue from value-added services such as tax filing and HR consulting. Some firms provide tiered packages, allowing you to choose the level of service that suits your needs. Upselling features like improved reporting and employee self-service can further increase their earnings. What Do Payroll and Benefits Do? Payroll and benefits management involves calculating employee wages, withholding taxes, and guaranteeing timely payments. It streamlines the payroll process, reducing administrative tasks for you. Moreover, it handles tax filings at various levels, preventing potential fines. Benefits administration integrates with payroll, managing health insurance and retirement contributions effectively. Conclusion In conclusion, partnering with a payroll firm can greatly simplify your business operations by managing payroll intricacies, guaranteeing compliance, and reducing the potential for errors. By outsourcing payroll, you can focus on your core business functions as you utilize the expertise and technology of professionals in the field. As you consider this option, evaluate your specific needs, budget, and the firm’s reputation to secure a successful collaboration that improves efficiency and security within your organization. Image via Google Gemini and ArtSmart This article, "What Is a Payroll Firm and How Can It Benefit Your Business?" was first published on Small Business Trends View the full article

US lacks firepower to provide $350bn in coverage needed to revive Strait of Hormuz transits, JPMorgan saysView the full article

Median rents rose at a greater rate than median sales prices in 55% of the 416 counties with sufficient data between 2025 and 2026, Attom found. View the full article

Climate change comes with serious financial risks, and those risks could affect your retirement account. Is it up to your employer, then, to protect your 401(k) from those concerns? That’s the question posed by a first-of-its-kind lawsuit, filed today in the U.S. District Court Western District of Washington. A former employee of Cushman & Wakefield has filed a lawsuit alleging that the real estate company breached its duties under the Employee Retirement Income Security Act (ERISA) by failing to protect its workers’ 401(k) savings from climate-related financial risks. “Though often misrepresented as a purely ethical issue, climate risk is actually a severe economic risk,” said Kimberly Blake, attorney at ClientEarth USA, which is representing the plaintiff, in a statement. “You cannot claim to be a prudent fiduciary while ignoring the biggest systemic threat to the global economy.” Called Kvek v. Cushman & Wakefield, the complaint could have far-reaching implications for the country’s $12 trillion retirement market. If successful, climate experts say, the lawsuit would mean that both asset managers and employers can no longer ignore the economic costs of climate change, and that they have a duty to invest retirement savings with that consideration. Climate financial risks Climate-fueled disasters can disrupt supply chains, damage infrastructure, and devalue investments—partcuarly those in fossil fuels. There’s a high likelihood that at least some of your retirement savings are invested in fossil fuels. Nearly one fifth of all U.S. fossil fuel stocks are owned by American’s retirement savings accounts, according to one estimate. But thousands of oil and gas assets are at risk of becoming stranded, because future climate change policies could make them unprofitable or even force them to shutter early. Some workers have said they’ve already missed out on billions of dollars in 401(k) returns because their accounts were invested in fossil fuels. A 2024 As You Sow report found that more than two million employees from 12 tech-sector companies, including Amazon, Google, Adobe, and more, could have earned an estimated $5.1 billion in additional returns had their companies decarbonized their retirement plan holdings 10 years ago. A “bad financial bet” to invest in fossil fuels Decarbonizing investment accounts has been a growing movement over the past decade. Also called divesting, the idea is to get pension plans, school endowments, and other assets under management to remove all fossil fuel investments from their portfolios. To some, divesting from fossil fuels is a moral imperative: People may not want to support coal companies or deforestation practices through their 401(k) contributions. But it’s also a financial one: It’s become “really clear in the data that it’s a bad financial bet to invest in the fossil fuel sector,” Heather Coleman, environment program director with Wallace Global Fund, told Fast Company in 2021. “Climate risk isn’t just about fossil fuel stocks or coastal real estate,” Blake notes in a statement: “It’s a broad, interconnected threat that touches huge parts of the economy.” In 2025 alone, climate-fueled disasters caused the United States $115 billion in damages. Every 1 degree Celsius of warming, one report found, costs the world 12% in GDP losses. The lawsuit could be a “pivotal turning point” Banks are aware of, and are attempting to manage, these climate-related financial risks. So are businesses like Cushman & Wakefield—which has said, according to the lawsuit, that “[w]ith assets increasingly exposed to rising sea levels and flooding, and challenges like extreme and unseasonal weather patterns, real estate professionals cannot afford to ignore climate change.” “The Company well knew,” the lawsuit continues, “that prudent financial stewardship and climate risk considerations went hand in hand.” And yet, it alleges, the company’s retirement plan fiduciaries ignored, or failed to evaluate, this concern. “What’s striking here is that Cushman & Wakefield understood these risks in its own business operations, but it failed to protect its workers’ retirement savings from the same dangers,” Blake said. A spokesperson from Cushman & Wakefield told Fast Company that it plans to fight the lawsuit. “This claim is a variation on widely asserted legal theories that have been prevalent for many years,” the spokesperson said in a statement. “We have thoughtful processes in place that are designed to give our plan participants a variety of prudent investment options. Once served, we will appropriately defend this case.” The lawsuit concerns one particular fund, alleging that Cushman & Wakefield failed to “evaluate, monitor, and remove the Westwood Quality SmallCap Fund, which exposes retirement savers to dangerous levels of climate-related financial risk while at the same time underperforming and charging unreasonably high fees,” according to ClientEarth. “When your employer offers you a set of retirement options, you assume they’ve done the work to make sure those options are sound. You pick a fund, you contribute every month, and you trust that someone is paying attention to the risks,” Renee Kvek, lead plaintiff and former employee for Cushman & Wakefield, said in a statement. If successful, though, the lawsuit could have implications for retirement accounts beyond that one fund. “Retirement savings are workers’ deferred wages. They represent decades of labor and trust in a system that is supposed to protect their future,” Amy Gray, Stand.earth climate finance associate director, said in a statement. “This case could be a pivotal turning point in how courts, companies and retirement funds view climate risk, and prove that protecting retirement security isn’t political. It’s responsible.” View the full article

Freshservice is the beating heart of your ITSM function, but that doesn’t mean it’s the only tool involved in this crucial workflow. IT teams sometimes have to do technical work in dedicated platforms outside Freshservice, while service requests might need to be exported to other tools for reporting and auditability. That’s where Freshservice integrations come in; they transfer data between Freshservice service requests and work items in other tools, streamlining your ITSM process. Here’s everything you need to know about Freshservice integrations. What is Freshservice? Freshservice is an ITSM (Information Technology Service Management) solution built with enterprise organizations in mind. It allows IT teams to centralize the services they give to employees or customers, keeping initial requests, essential context, and resolutions in one place. With strong AI-powered features, it’s become a powerhouse for IT teams. What is Freshservice integration? A Freshservice integration connects Freshservice with other tools, bridging the gap between them so IT teams can collaborate more efficiently with other teams without long email chains or manually copying and pasting data between tools. These integrations can automatically push data from service requests to software development platforms, give leaders more visibility on IT trends, and more. Freshservice is often integrated with tools like: CRM tools like HubSpot and Salesforce. Software development platforms like GitHub and Azure DevOps. Project management platforms like Jira, Asana, and Smartsheet. Other customer support or service apps like ServiceNow and Zendesk. Why does Freshservice integration matter? Integrating Freshservice with other tools creates significant benefits for your IT teams, such as: Improved average resolution time: When IT teams don’t have the context they need, service requests get stuck. Integrating Freshservice with other tools means IT teams know what’s happening in other tools even as they work. Better SLA compliance: For IT teams that provide services to external customers, SLAs are an essential metric. Integrations ensure essential information isn’t lost in translation, allowing IT teams to respond to issues more quickly. Stronger reporting: Freshservice is a great tool, but it’s not always the best platform to report on your IT team’s work. With the right integration, you can sync Freshservice data to your reporting tool of choice so stakeholders have more visibility on ITSM trends. Fuller context: Integration solutions let you centralize data from multiple tools in Freshservice, meaning IT teams can have access to everything from customer support tickets in other tools to software development projects and more. 4 types of Freshservice integration Not all Freshservice integrations are the same. Some are designed to cover basic functionality while being easy to set up and use. Others allow for deep integration (i.e., supporting more fields and automating more actions) but require deep involvement from a technical team and significant time to deploy. Meanwhile, iPaaS solutions like Unito strike a balance between the two extremes, allowing you to get the best of both worlds. Here are some of the most popular types of Freshservice integration: Built-in Freshservice integrations: The Freshservice app store offers integrations built by the Freshservice team for tools like Microsoft Teams and Slack, allowing updates and work from Freshservice to be pushed to these platforms automatically. Automation platforms: Tools like Zapier use if-this-then-that logic to automate simple actions across thousands of apps. This logic means Zapier users can easily build integrations for Freshservice in just a few clicks. While these integrations are more limited than other options, their sheer availability and ease-of-use are serious advantages. iPaaS: An iPaaS solution like Unito builds relationships between work items in Freshservice and other tools, allowing data from over 100 fields in Freshservice to sync automatically to other tools you use. These platforms allow for seamless collaboration across Freshservice and other tools. Agentic AI: Freshservice’s Freddy AI Agent can automatically take actions not just in Freshservice, but across e-commerce tools, social media platforms, and more. These AI agents are a natural first step for Freshservice users looking to automate repetitive tasks. How to integrate Freshservice with Unito Here’s a look at how an integration between Freshservice and other tools works with Unito. Step-by-step integration guide Connect tool accounts to Unito: After signing up for Unito, click +Create Flow and connect Freshservice and the tool you’re integrating to Unito. Choose flow direction: Most Unito integrations support both one-way and two-way flows. Unito’s Freshservice integration only syncs data from Freshservice out to other tools, streamlining how work is dispatched to IT teams. Set rules: Unito rules use trigger-action logic to filter out work items you don’t want synced or automate certain actions. Build a rule by setting the trigger Unito should look for and the action you want it to take. Map fields: In most flows, Unito can automatically map fields in Freshservice with fields in other tools. From there, you can customize these mappings so they match your workflow exactly. Launch your flow: Once you’ve mapped your fields, your flow is ready to launch. After an initial sync, Unito will check for changes in real-time. Want to know more? Check out this in-depth guide to integrating Freshservice with Jira. Challenges to watch out for when integrating Freshservice Before you pick an integration, consider the following challenges. Deployment times and technical resources Some integration solutions are built to support just about every field in Freshservice and any tool you might need to integrate it with. But actually implementing these platforms can potentially take weeks, if not months, as they’re configured to your specific needs. That implementation also requires significant technical knowledge, whether that comes from your own IT team, third-party consultants, or an integration vendor. Choosing the wrong integration solution can be a significant investment that takes time to achieve a positive ROI. Integration depth A “deep” integration supports more fields and can automate more actions than other integrations. While a deeper integration can seem like a better investment on the surface, that’s not always the case. Deeper integrations typically involve more complex and lengthy deployment — with only a few exceptions. When researching an integration solution, make sure it’s deep enough to support your workflows without reaching a level of complexity that makes it too difficult to use. Authentication and security Integration solutions need to access Freshservice and the other tools you use, often in the same way you access them. This creates an inherent security vulnerability you wouldn’t have to worry about without an integration. That said, most integration vendors are aware of this, ensuring data security with measures like data encryption and prompt incident response. How to keep Freshservice integrations secure Freshservice integrations move data between service requests and work items in other tools, which makes data security especially important.. Here are some things to keep in mind so you can keep these integrations secure. Compliance Depending on your industry and your jurisdiction, the way you handle data has to comply with various regulations and best practices. If you’re located — or have customers in — California, for example, you have to abide by the state’s California Consumer Privacy Act (CCPA). Similarly, specific industries have their own regulations, like the Health Insurance Portability and Accountability Act (HIPAA). When researching integration solutions, make sure they comply with any regulations you need to abide by. Access control Access control refers to measures you take to limit who can and can’t access a platform. Before you deploy an integration solution, you need to consider two things: Who will have access to it. If the platform can support the level of access control you need. Some platforms allow for precise, role-based access control, while others are more limited. Make sure the platform you choose meets your needs. Security certifications Certifications like SOC 2 Type 2 are data security frameworks that achieve two things: Give companies guidelines to follow for keeping data safe. Allow vendors to show their commitment to data security. When you’re researching integration vendors, review their security certifications to ensure their commitment to data security matches yours. Best practices when integrating Freshservice When rolling out your first Freshservice integration, consider these best practices: Start with a small pilot project between a few Freshservice service requests and work items in another tool. This lets you get a feel for an integration solution before it has access to all your organization’s data. Evaluate the results of a pilot project before implementing integrations at scale. That way, you can customize an integration solution to support your workflows based on what you’ve learned. Consider if you need an integration solution that requires technical knowledge to use — restricting access to IT teams — or if you need something more accessible for all teams. Review the integration vendor you’ve chosen at least yearly to ensure they’re competitive compared to the broader market. Use built-in Freshservice integrations where possible to enhance any third-party integration solution you use. Ready to integrate Freshservice? Meet with Unito product experts and see what a two-way integration can do. Talk with sales View the full article

President Donald The President on Thursday fired his embattled Homeland Security Secretary Kristi Noem, after mounting criticism over her leadership of the department, including the handling of the administration’s immigration crackdown and disaster response. The President, who said he would nominate Oklahoma Republican Sen. Markwayne Mullin in her place, made the announcement on social media after Noem faced a two-day grilling on Capitol Hill this week from GOP members as well as Democrats. Noem’s departure marks a stunning turnaround for a close ally to the president who was tasked with steering his centerpiece policy of mass deportations. But she appeared to increasingly become a liability for The President, with questions arising over her spending at her department and over her conduct in the aftermath of the shooting deaths of two protesters in Minneapolis earlier this year. The President said he’ll make Noem a “Special Envoy for The Shield of the Americas,” a new security initiative that he said would focus on the Western Hemisphere. Noem, who appeared at a law enforcement event in Nashville, Tennessee, moments after The President’s announcement, did not address her ouster there. She read from prepared remarks and was not asked by attendees about the development. Later, in a social media post, she thanked The President for the new appointment and touted her accomplishments as secretary. “We have made historic accomplishments at the Department of Homeland Security to make America safe again,” she wrote. The administration’s immigration crackdown faced criticism, especially in Minnesota Noem is the first Cabinet secretary to leave during The President’s second term. Her tenure looked increasingly short-lived after hearings in Congress this week where she faced rare but blistering criticism from Republican lawmakers. One particular point of scrutiny was a $220 million ad campaign featuring Noem that encouraged people in the country illegally to leave voluntarily. Noem told lawmakers that The President was aware of the campaign in advance, but The President disputed that in an interview Thursday with Reuters, saying he did not sign off on the ad campaign. Noem has faced waves of criticism as she’s overseen The President’s immigration crackdown, especially since the shooting deaths of the two protesters in Minneapolis at the hands of immigration enforcement officers. The former South Dakota governor was also criticized over the way her department has spent billions of dollars allocated to it by Congress. Her department, DHS, has been at the center of a funding battle in Congress over immigration enforcement tactics and has been shut down for 20 days, although many of the employees are continuing to work, often without pay. Even before Noem’s appearance before key congressional committees this week, Republican lawmakers had been anticipating the secretary’s eventual ouster, particularly after her handling of the immigration enforcement crackdown in Minneapolis. As they tried to end the ongoing Homeland Security shutdown, Senate Republicans had noted privately to Democratic senators that Noem was likely on her way out and that that should prompt Democrats to move forward with agreeing to fund the department again, according to two people familiar with the discussions. Democrats did not see that as an actual concession by Republicans, considering Noem was becoming a political liability for the GOP, said the people, who spoke on condition of anonymity to discuss private negotiations. DHS leadership changes come at a pivotal time Aside from immigration, Noem also faced criticism — including from Republicans — over the pace of emergency funding approved through the Federal Emergency Management Agency and for the The President administration’s response to disasters. Mullin would need to be confirmed by the Senate, but under a federal law governing executive branch vacancies, he would be allowed to serve as an acting Homeland Security secretary as long as his nomination is formally pending. Voting in the Senate just after The President’s announcement, Mullin said he has “no idea” how quickly his nomination will move. “The president and I are good friends. So we look forward to working closer with the White House, and obviously I’m gonna be over there a lot more,” he said. Mullin would need to be confirmed by the Senate, but under a federal law governing executive branch vacancies, he would be allowed to serve as an acting DHS secretary as long as his nomination is formally pending. Mullin would take over the third-largest department in government that has responsibility for carrying out The President’s hardline immigration agenda. And he would assume the role at a pivotal time for that agenda. Immigration enforcement during the first year of The President’s administration was largely defined by high-profile, made-for-social-media operations with flashy names, often led by Border Patrol commander Gregory Bovino, who reported directly to Noem. Noem herself often went out on those operations, riding along with officers when they went out to make arrests. But those high-profile operations in places like Los Angeles, Chicago and Minneapolis often led to clashes with activists and protesters that were captured on video and drove opposition to the president’s immigration agenda. That culminated with the shooting deaths in Minneapolis after which The President shuffled leadership of the operation. The number of officers there was drawn down shortly after. —Michelle L. Price and Rebecca Santana, Associated Press Associated Press writers Seung Min Kim and Mary Clare Jalonick contributed. View the full article

Last week, Block CEO Jack Dorsey shared that his fintech company would be cutting 40% of its workforce, arguing that AI would allow them to do more with smaller teams. Many observers wondered if the large-scale layoffs reflected the new reality amid rapid AI adoption, and whether it was just a matter of time before other companies followed suit. But not everyone is buying it. In a post on X, Whoop CEO Will Ahmed shared that his company—which makes health and fitness wearables—would be nearly doubling its 800-employee headcount this year, drawing a contrast with employers that have been slashing jobs over the last year. He then weighed in on the growing trend of companies using AI to explain their layoffs. “Investing in talent and AI tools not mutually exclusive,” Ahmed wrote. “Many of these ‘AI layoffs’ are just companies underperforming or lacking a bigger market opportunity.” In an interview with Bloomberg, Ahmed elaborated on this idea. “There’s a lot of companies that are doing layoffs right now and blaming it on AI,” Ahmed said. “But they’re actually doing layoffs because the businesses aren’t performing particularly well. And it’s a convenient excuse.” Amid waves of layoffs across corporate America, many leaders and CEOs have been reticent about their reasons for trimming headcount, often gesturing at AI investments when they announce layoffs. In 2025, AI was cited in nearly 55,000 layoffs, according to outplacement firm Challenger, Gray & Christmas—and some leaders, like Dorsey, have been quite explicit about pinning job losses on AI. Economists, on the other hand, have pointed out that there are countless reasons driving layoffs, from immigration policy to tariffs and political uncertainty; sometimes it’s simply a matter of financial performance. As a former Block employee noted in the New York Times this week, a closer look at Block’s job losses—which reportedly included cuts to the policy team and DEI roles—indicates that the layoff strategy was likely driven more by traditional cost-cutting measures. Some experts have argued that, like many tech companies, Block overhired during the pandemic. (There’s also limited evidence that AI is causing a broad contraction in the workforce: Goldman Sachs economists recently estimated that the sectors most impacted by AI had seen 5,000 to 10,000 monthly net job losses last year.) Connecting layoffs to AI, however, offers a more palatable framing for shareholders. Ahmed is not the only CEO who has started talking about this more openly. Recently, OpenAI CEO Sam Altman made similar remarks when interviewed during an AI conference in India. “I don’t know what the exact percentage is, but there’s some AI washing where people are blaming AI for layoffs that they would otherwise do, and then there’s some real displacement by AI of different kinds of jobs,” he said. “I expect we’ll see more of the latter over time.” But for every CEO willing to admit that AI isn’t the sole factor driving layoffs, there are plenty of others who keep touting its potential—and if the 15% jump in Block’s share price is any measure, they seem incentivized to continue doing so. View the full article

If you're shopping online, or just trying to access certain websites, and things aren't loading properly, it's (probably) not your internet: Amazon is down. As of Thursday afternoon, Amazon services, including both Amazon.com and AWS, are having issues loading and running. You can see that from Downdetector, a site used to track user reports of issues with websites and services. (Disclosure: Lifehacker's parent company, Ziff Davis, owns Downdetector.) As of this article, Amazon.com has tens of thousands of user reports, while AWS has thousands. Prime Video, Amazon's streaming service, also has user reports at this time, though they aren't as drastic. It's not clear what's causing the issues just yet, but it's not the first time Amazon has made headlines for outages. Back in October, AWS had a major period of downtime, taking down much of the internet. Many sites and services rely on AWS to operate, so when Amazon has issues, all of these companies have issues too. I'm sure we'll learn what the issue is in due time, and Amazon will undoubtedly issue a fix soon after. But it goes to show that even the largest companies in the world aren't immune to problems. And when those problems do arise, it affects a lot of users. View the full article

On February 27, Defense Secretary Pete Hegseth took the unprecedented step of designating a U.S. firm—Anthropic—as a supply chain risk. Anthropic’s crime? It refused to violate industry-wide protocols against using AI for mass surveillance or autonomous weapons. Hegseth’s designation, which has until now been reserved for foreign firms, bars U.S. military contractors from doing business with the company. President The President also vowed to excise Anthropic’s products, which are often regarded as superior for government applications, from federal agencies altogether. He also smeared the company’s leaders as “Leftwing nut jobs.” This style of harsh retribution is emblematic of crony capitalism, which appears to be infecting the U.S. economy from the top down. The The President administration seems to be reshaping the market via executive actions that include a chaotic tariff scheme filled with favoritism; priority regulatory approvals for political allies, such as in the sale of TikTok to political donors; seemingly compelled business contributions to personal and political causes, including the new White House ballroom; taking government equity stakes in companies like Intel and U.S. Steel; and punishing companies like Anthropic that resist his demands. All the while, the president and his family have reportedly profited by billions of dollars. The Wall Street Journal recently discovered a $500 million investment from the United Arab Emirates into the The President family crypto business—just weeks before the administration granted the U.A.E. access to closely controlled AI chips. The U.S. recently fell to its lowest-ever ranking in a leading global corruption index. CRONYISM AND CAPITALISM This behavior risks grave economic consequences. From the Heritage Foundation to the Cato Institute, analysts agree: Crony capitalism is bad for business. By short-circuiting market-based competition, it shrinks investment, derails innovation, and weakens the overall economy. As it eliminates traditional boundaries between government and business, the administration seems to be taking the U.S. down a path increasingly like authoritarian governments like Hungary and Russia. Symptoms of crony capitalism include: Meritocratic capitalism, disrupted: Competition no longer drives success. Instead, investment is determined by connections and political alignment. Authorities asking favors: Political leaders provide compliant companies with benefits such as insider contracts, licenses, or regulatory loopholes. A culture of corruption: Businesses focus on demonstrating loyalty to leaders to secure benefits. Regulatory favoritism: Regulators protect crony companies rather than applying the rules fairly. Government bailouts: Instead of being allowed to fail, favored companies get subsidies, regulatory interference on their behalf, and tax breaks. Crony capitalism has crippled entire countries. Hungary, once a leading Eastern European economy, now suffers from low growth and talent flight as the president’s cronies appear to mismanage national industries. Turkey, once a promising market, now contends with hyperinflation as the president’s seemingly under-qualified allies fill key banking and corporate roles. And Russia, whose industries are controlled by Putin’s oligarchs, has less economic output than Texas or California. HOW BUSINESSES ARE APPROACHING IT American business leaders are taking different approaches to the problem. Some are seeking advantage by playing the game. In crony capitalist economies, a few favored companies may surge temporarily. But they eventually get burned. Crony participants risk a ruined brand, a talent exodus, or a change of fortune under an erratic leader or a future opposition government. Other leaders are hoping to wait out the storm, while privately lamenting a system that feels increasingly arbitrary, politicized, and unsafe to challenge. In an October 2025 Leadership Now Project/Harris Poll, 84% of executives said they were concerned that the political and legal climate was impacting their business and injecting harmful uncertainty into markets. Citadel CEO Ken Griffin recently summed up the sentiment, noting that “most CEOs just don’t want to find themselves in the business of having to…suck up to one administration after another to succeed in running their business.” But publicly, most executives stay quiet, fearing retaliation. More than a third of those we surveyed admit they are very or somewhat uncomfortable speaking publicly on policy. Yet nearly all—93%—believe companies should push back against damaging government actions. THE ROLE OF INDUSTRY ASSOCIATIONS Fortunately, there’s an answer: Business leaders can activate their industry associations. We believe the time is right for business leaders to protect their industries and our democracy by taking three powerful steps: 1. Make a clear statement opposing cronyism. For example, “Our industry commits to doing business in the following fair and constructive ways, and we are united against the following anti-competitive government behaviors.” To get wide exposure, associations can harness their public policy committees, PR professionals, legal advisory, and media access. 2. Encourage members to reinforce this message. Association leadership can encourage member companies and executives to publicly support the association’s statement. They can facilitate this by providing talking points, PR support, and publicly standing behind members’ comments. 3. Include a statement supporting election legitimacy. As we approach the November midterms, industry leaders can reaffirm that going to the polls is a civic duty. Though today’s circumstances are unprecedented, this isn’t a new approach. American industries have for generations relied on associations to tackle collective challenges. And associations are experienced at educating the public about policy matters. And they are already beginning to step up. Entertainment industry groups took on the Jimmy Kimmel firing. The American Bar Association addressed what it called the administration’s “law firm intimidation policy.” And the Professional Services Council, which represents government contractors, has issued warnings that the administration’s actions are undermining merit-based contracting. Taking action as part of an association is in the interest of many companies. There is strength in numbers. And, as most executives learn over their careers, fair rules and honest referees make industries function well for all stakeholders. With America’s competitiveness and market access under threat, we believe this moment demands a decisive response. Executives already have the tools they need to protect themselves and safeguard their industries. Working and standing together, they can help stop crony capitalism—before the contagion grows too hard to resist. Daniella Ballou-Aares is CEO of Leadership Now Project. Marc Metzner is a retired management consulting partner who works closely with Leadership Now. View the full article

Eli Lilly wants to get its obesity drugs into the hands of more Americans and it’s betting on employers to help do so. The Indianapolis-based drugmaker launched a new program on Thursday that’s designed to offer employers more options for covering obesity drugs, thereby lowering the cost barriers to access for employees. Lilly and rival Novo Nordisk have taken various steps in recent months to slash the prices of their now-popular GLP-1 medications, and Lilly’s latest move is intended to close what it refers to as an “access gap” in U.S. obesity care. In early 2024, Lilly launched LillyDirect, an online pharmacy where patients can buy a variety of medications, including its popular Zepbound injectable for obesity, without using insurance. By creating a similar platform, this time focused on employers, the drugmaker wants to give companies “greater cost predictability and transparency” to expand coverage of these drugs. Employers frequently express concern about the costs of these medications. “By enabling coverage outside traditional benefit designs, we lower barriers to treatment and give employers greater control over how they support employee access to obesity care,” Kevin Hern, senior vice president of Lilly Employer, said in a statement. “This innovation can help employees access authentic obesity management medicines with more affordable out-of-pocket costs.” FOCUS ON LOWERING DRUG PRICES Through this new employer platform, Lilly’s Zepbound drug could be made available to employers for a discounted price of as little as $449 per month, which compares to its list price of more than $1,086. Zepbound was approved by the Food and Drug Administration in 2023 for chronic weight management. Even as drugmakers are racing to lower the costs of GLP-1 and other obesity medications, Americans are generally paying more at the pharmacy this year. In January and February, the prices of 961 brand-name drugs went up by a median of 4%, according to an analysis by 46brooklyn, a drug price research firm. That said, price increases are typical at the start of a year. WHY LILLY IS TARGETING EMPLOYERS Lilly’s new program will allow employers to choose from more than 15 administrators to design benefits programs that are tailored to the needs of their workforce—and expand access to obesity coverage. A 2025 survey by the International Foundation of Employee Benefit Plans found that while coverage of GLP-1 medications is relatively common for the treatment of diabetes, with 55% of employers providing coverage, only 17% of those employers said they were considering offering coverage for weight loss, as well. “I think we’ll learn in the coming months ahead, if this is a solution that maybe enables some employers who have been sitting on the sidelines to opt into obesity coverage for their employees,” Hern told CNBC. Some employers could add coverage for these drugs this year or wait until 2027, he added. As it did in November when it teased the new employer platform was coming, Lilly appealed to the business sense of employers—citing a 2018 figure that estimates annual obesity-related costs resulted in $1.24 trillion in lost productivity. LILLY’S MOVES Thursday’s announcement is but the latest by Lilly in its race to overtake Novo Nordisk in the GLP-1 race. Last month, it unveiled its latest innovation—the KwikPen—which contains a month’s worth of Zepbound. But like much of the U.S. stock market, shares of Lilly have been caught up in a selloff in the wake of the U.S. and Israeli strikes on Iran. While the S&P 500 Index was down about 1.4% in mid-day trading on Thursday, Lilly’s stock slumped nearly 5%. View the full article

Democratic attorneys-general say president exceeded authority after Supreme Court struck down original leviesView the full article

We may earn a commission from links on this page. Deal pricing and availability subject to change after time of publication. Apple announced new products this week, all of which are all already available for preorder and will be released on March 11. But before you place your order, it's best to compare the preorder deals from various retailers. Walmart has the best iPad Air preorder deal, for example, offering $40 off the listing price. If you're interested in a new MacBook, however, Best Buy is the place to look: it's the only major retailer offering any preorder deals for all three new MacBooks. Apple A18 Pro chip with 6‑core CPU and 5‑core GPU - 8GB Memory - 256GB SSD - Indigo 13-inch MacBook Neo ($25 Best Buy Gift Card) $599.00 at Best Buy Pre-order Here Pre-order Here $599.00 at Best Buy Apple M5 chip with 10-core CPU and 8-core GPU - 16GB Memory - 512GB SSD - Midnight 13-inch MacBook Air ($50 Best Buy gift card) $1,099.00 at Best Buy Pre-order Here Pre-order Here $1,099.00 at Best Buy Apple M5 Pro chip with 15-core CPU and 16-core GPU - 24GB Memory - 1TB SSD - Space Black 14-inch MacBook Pro ($100 Best Buy gift card) $2,199.00 at Best Buy Pre-order Here Pre-order Here $2,199.00 at Best Buy SEE 0 MORE Previously, anyone looking for an affordable MacBook looked toward older models, like the M1 and M2. The MacBook Neo is changing that, along with the personal computing market in general. The Neo isn't breathtaking in specs—it has the A18 Pro processor, the same chip as the iPhone 16 Pro—but the price is what makes it enticing. At $599, I can see a lot of people opting for this MacBook over other budget laptops, especially with the $25 pre-order gift card. (You should still consider the M1 and M2 as good options if you find good deals, though.) The Neo is also missing some premium features you'd expect from other MacBooks, like a backlight for the keyboard, Touch ID, and MagSafe charging. It's also limited to 8GB of RAM, which in today's standards, is subpar. The M5 MacBook Air is tempting, starting at $1,099, but you shouldn't be swayed if you already own an M4. The rest of the laptop is virtually the same. Of course, the basic starting model doubles the storage to 512GB, which is nice and only $100 more than the listing price when the M4 was released. Add a $50 gift card, and this is a great option for someone upgrading from the M2, M1, or getting their first MacBook. The new MacBook Pro is a beast. It starts with 1TB of storage, the M5 Pro chip with a 15-core CPU and a 16-core GPU. The RAM is 24GB, which offers people all the multitasking and smoothness they need to run multiple programs at once. Starting at $2,199, the $100 gift card doesn't soften much of the blow, but it's $100 more than anyone else is offering. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods 4 Active Noise Cancelling Wireless Earbuds — $119.00 (List Price $179.00) Samsung Galaxy S26, Unlocked Android Smartphone + $100 Gift Card, 512GB, Powerful Processor, Galaxy AI, Immersive Viewing, Durable Battery, 2026, Black — $899.99 (List Price $1,199.99) Samsung Galaxy Buds 4 AI Noise Cancelling Wireless Earbuds + $20 Amazon Gift Card — $179.99 (List Price $199.99) Google Pixel 10a 128GB 6.3" Unlocked Smartphone + $100 Gift Card — $499.00 (List Price $599.00) Apple iPad 11" 128GB A16 WiFi Tablet (Blue, 2025) — $329.00 (List Price $349.00) Apple Watch Series 11 [GPS 46mm] Smartwatch with Jet Black Aluminum Case with Black Sport Band - M/L. Sleep Score, Fitness Tracker, Health Monitoring, Always-On Display, Water Resistant — $329.00 (List Price $429.00) Amazon Fire TV Soundbar — $99.99 (List Price $119.99) Deals are selected by our commerce team View the full article

Delay in deploying HMS Dragon to Cyprus has triggered criticism of UK’s defence capabilityView the full article

DHS leader was seen as responsible for the administration’s heavy-handed immigration crackdown View the full article

Google AI Max drives revenue but at a higher cost, according to Smarter Ecommerce’s Mike Ryan, who analyzed 250+ campaigns. Outcomes vary, and much more testing is still needed. Why we care. AI Max isn’t a minor update. It’s Google’s most significant reimagining of Search campaigns in years, shifting away from keyword syntax toward pure intent matching. For you, that’s both an opportunity (possible growth) and a risk (an efficiency tradeoff). By the numbers. The result of the analysis: Median revenue: +13% Median CPA: +16% ROAS range: +42% to -35% Advertisers who activate AI Max typically see 14% more conversions or conversion value at a similar CPA or ROAS, rising to 27% for campaigns still relying on exact and phrase match keywords, Google says. Turning on AI Max is essentially a coin toss: you may see a lift, but efficiency likely won’t follow, Ryan concluded What AI Max actually is. Rather than forcing Search campaigns into Performance Max, Google went the other direction — bringing PMax-style automation into classic Search. The result is three core features: Search Term Matching (broad match expansion plus keywordless targeting), Text Customization (dynamic ad copy), and Final URL Expansion (automated landing page selection). Four pitfalls Smarter Ecommerce identified: Broad match cannibalization: Up to 63% of the time, recycling existing coverage rather than finding new queries. Competitor hijacking: In one account, AI Max scaled so aggressively into competitor brand terms that it consumed 69% of total Search impressions. Reporting overload: Search term and ad combination reports can run to tens of thousands of rows, making manual auditing nearly impossible without automation. Search Partner Network blowouts: One campaign saw half a million monthly impressions land on SPN at a 0.07% conversion rate, versus 3.04% on standard Google Search. Between the lines. Google’s 14% uplift stat conspicuously excludes retail — an omission Ryan flags as significant for ecommerce advertisers. There’s also a deeper irony: you’re most likely to adopt AI Max if you’re already running Broad Match, DSA, and PMax — yet Google says those accounts will see the lowest incremental benefit. What’s next. In a conversation with Ryan, Google Ads Liaison Ginny Marvin confirmed that Google plans to deprecate Dynamic Search Ads and migrate the technology into AI Max for Search. No firm timeline was given, though past Google deprecations often run about a year from announcement. Ryan recommends activating AI Max’s keywordless features in your existing Search campaigns now and beginning to wind down DSA — not migrating it to PMax. Ryan’s verdict is cautious optimism. About 16% of advertisers are testing AI Max, and few have gone all in. Start small, audit aggressively, and don’t let FOMO around AI Overviews drive your decision. The report. The Ultimate Guide to AI Max for Google Search View the full article

For a word that determines so much of how social media and the creator economy operate, engagement can be pretty hard to pin down. So, we looked at the data. This report documents how engagement works across social media in 2026. Not how we wish it worked or not how platforms market it — but what the data shows. To understand what's actually happening across feeds right now, we dug into tens of millions of posts published through Buffer — looking at engagement baselines, reply behavior, posting frequency, and how different formats perform across platforms. The short version: If you're spending more energy looking for the perfect time to post than you are replying to the people who showed up, the data suggests you might be overthinking it. Engagement means something different on every platform, and the most powerful thing a creator can do isn't about format or posting time — it's talking back to the people engaging with them. (As long as you're still posting consistently; that matters too.) Across six platforms and nearly two million posts, accounts that reply to comments consistently outperform those that don't — by as much as 42% on Threads and 30% on LinkedIn. That doesn't mean replies cause engagement. But it's one of the strongest patterns we found, and — we think — one of the most untapped. Beyond replies, things get messier. Typical engagement rates vary by more than 2x between the highest and lowest platforms. Year-over-year movement is split between platforms that are climbing and those that are dropping, and the reasons aren't always what you'd expect. Format performance varies a lot from platform to platform — what works on one network doesn't necessarily translate to another. (We learned that one the hard way across our own channels.) We built this report to be a reference, not a rulebook. The baselines can help you understand what "normal" looks like, so you can set more realistic goals. We had a lot of fun putting this together — and we're already applying what we learned across Buffer's channels and our own. We hope it's as useful for you as it's been for us. Explore the data on our Insights page → Jump to a section: Key findings at a glance Methodology The reality of engagement in 2026 The effect of replies on engagement The differences in engagement by content type: A platform-by-platform breakdown Timing and frequency can boost engagement - but not drive it What this means Key findings at a glanceEach finding below gets its own section later in the report — we've included cross-references so you can skip straight to the parts that matter most to you. How to use this reportWe really wanted this report to be practical — something you could actually use in your social media strategy. With that in mind, here's where I'd start: Check baseline engagement rates first. "Typical" engagement looks pretty different from platform to platform — and the numbers aren't directly comparable across networks. Knowing what normal looks like makes everything else in this report more useful.Then dig into the platforms you care about. The format and approach breakdowns get specific. What works varies more than we expected.Save timing and frequency for last. They matter, but they're a secondary layer. Timing and frequency are worth optimizing once you know what content is landing, but not the place to start.The baseline reality: 'engagement' isn’t just one thingBefore we get into what works and what doesn't, let's get clear on what "engagement" even means — because it's not the same on every platform. Each network defines it differently — LinkedIn, for example, includes clicks in its engagement rate, while most other platforms don't — and some don't even provide the inputs for a comparable engagement rate. These medians represent typical performance in Buffer's dataset, rather than universal benchmarks. (We wish universal benchmarks existed. They don't.) 2025 baseline for engagement across platformsIn Buffer’s cross-platform dataset, typical engagement rate is clustered into tiers: Higher median engagement: LinkedIn (~6.2%), Facebook (~5.6%), Instagram (~5.5%)Mid-tier: TikTok (~4.6%), Pinterest (~4.0%), Threads (~3.6%)Lower median engagement: X (~2.5%)Engagement is uneven — and it’s shiftingYear over year (from 2024 → 2025), platforms moved in different directions: Up: X (~+44%), Pinterest (~+23%), Facebook (~+11%)Flat-ish: TikTok (+~3%)Down: LinkedIn (~-5%), Threads (~-18%), Instagram (~-26%)A word of caution on these numbers: a drop in engagement rate doesn't mean a platform is in decline. It could reflect changes to the algorithm, a shift in who's posting or how often, or simply that the platform is growing and engagement hasn't caught up yet. Instagram for example, has increasingly steered creators toward views as its primary success metric over the last year, which means the traditional engagement rate formula may be measuring less of what Instagram is actually optimizing for. Equally, a rise doesn't automatically mean a platform is thriving for everyone. As Julian Winternheimer, Buffer's data lead, notes: "The dramatic changes in some metrics — particularly X's 44% increase, which led to a move from a lower baseline (1.96% to a 2.83% median engagement rate)likely reflect changes in the user base or metric definitions rather than genuine performance improvements." Buffer's growing, evolving user base can also play a part in these shifts, he adds. "The composition of accounts changes, which can have a bigger impact on medians than actual platform performance." Year-over-year changes can point us in the right direction when it comes to understanding platforms, but they don't tell the whole story on their own. Replying works on every platformOne behavior showed up consistently across very different networks: posts where creators or brands reply to comments tend to earn more engagement than posts where they don't. We expected this to be true on some platforms — we didn't expect it to hold up on all six. Estimated engagement lift when replies are present: Threads: +42%LinkedIn: +30%Instagram: +21%Facebook: +9%X: +8%Bluesky: +5%Now, we can't say with absolute certainty that replying causes higher engagement. It's possible that posts that perform well naturally attract more comments, and creators are then more likely to reply because there's more activity to respond to. But the analysis compares each account against its own baseline, not against other accounts. And the same pattern showed up across all six platforms, which honestly isn't something we see often in this kind of data. However, that's where the consistency ends. Formats don’t translate from platform to platformFormat performance varies a lot from platform to platform — what works on one network doesn't necessarily translate to another. And sometimes the answer changes depending on whether you're optimizing for reach or engagement on the same platform. A few highlights: Instagram behaves like two platforms. Reels get 36% more reach than carousels — but carousels earn 12% more engagement. Part of this split comes down to how engagement rate is calculated: Reels are optimized for views and reach, which dilutes their per-impression engagement rate. Depending on your goals, those are two different strategies.LinkedIn is carousel-dominant for engagement. Carousels earned a median engagement rate of 21.77% — roughly three times that of video and images. Even a below-average carousel performs about as well as a typical video or image post.Threads rewards visuals more than its "text-first" positioning suggests. However, there's enough overlap across formats that any type of post can do well.Facebook's format gaps are tiny. Images, video, and text all land within one percentage point of each other. Format matters less here than almost anywhere else.X is increasingly tiered. Text posts lead in engagement, but the Premium divide matters more than format here. After January 2025, Premium and regular account engagement rates split sharply — and in the most recent months of the study, the median engagement rate for regular accounts hit 0%.Timing and frequency are amplifiers of engagementTop-performing accounts publish more often and more consistently than the median account. But there's no single "best time to post" or magic number of posts per week that works across platforms, niches, account sizes, or teams (though we can make some per-platform recommendations for when posts tend to perform well). What we can say: going quiet has an impact.. In our frequency analysis of 4.8 million channel-week observations, accounts that didn't post in a given week consistently underperformed their own baseline growth rates. Any posting was better than not posting at all, and that held across platforms. Posting more often gives you more chances to be seen. Posting at the right time improves those chances. But the biggest lever is still creating content people genuinely want to engage with. MethodologyWe know methodology sections aren't the reason anyone opens a report. But if you're the kind of person who wants to know how the sausage gets made — or if you're planning to cite any of these numbers — this is for you. Every section of this report rests on the same dataset, the same metric definitions, and the same interpretation rules. When a specific section departs from these defaults, we note it. Data sources and scopeSources: Posts published through Buffer across the platforms included in this report. Across the studies that inform this report, which includes tens of millions of posts — from 18.8 million X posts in the Premium analysis, to 15.7 million posts in the frequency and engagement study, to nearly 2 million posts across six platforms in the reply analysis. What this represents: Buffer users and Buffer-posted content only. It's not a full-platform view of any network, and we don't treat it as one. Time windows: Unless otherwise stated, cross-platform baselines use 2025 data with year-over-year comparisons to 2024. Our most recent data runs through December 3, 2025. Some platform deep-dives use different windows based on the underlying study (e.g., the Instagram format analysis that uses January 2022 – October 2024). Eligible accounts (baseline and trend analyses): To reduce noise from dormant or one-off posting, accounts must meet minimum activity thresholds — posted at least 10 times in the past year, across at least 4 different weeks. What 'engagement' means on each platform"Engagement" is platform-defined. Where possible, we use engagement rate. Where that isn't available, we use the strongest proxy the platform provides. Engagement components by platform (as used in this report): Instagram: likes + comments + shares (format analysis may also reference Instagram's broader definition, which includes saves, where available)Facebook: reactions + comments + sharesX: likes + retweets/reposts + commentsLinkedIn: total engagementsThreads: likes + reposts + replies + quotesTikTok: engagement rate (engagements ÷ reach)Bluesky: likes + comments + repostsEngagement rate definition (when available): engagements ÷ reach, where reach is the number of unique accounts that saw the post. This is closely aligned to the data we have for each platform in Buffer. However, it's worth noting that not every platform defines or reports reach the same way. Where reach isn't available, we use the closest equivalent (like impressions or views). These components vary in intent — a save and a reply are very different behaviors. We get into that more in the caveats below. Why we use medians (and when we don't)Across the report, we default to median metrics. Social performance distributions are heavily skewed — a small number of viral posts and very large accounts can pull averages far from what most people actually experience. Medians give a better picture of what "typical" actually looks like. The exception: When the question is about relative change within the same account (e.g., does replying to comments correlate with better performance for this account?), we use within-account modeling — fixed-effects regression and z-score analysis — rather than aggregate medians. This lets us compare each account to itself over time, which is a fairer test than comparing accounts of very different sizes to each other. Several of the studies below use this approach; we'll note the specifics (dataset size, platforms, validation) in each one rather than repeating the full explanation. What you can (and can't) compare across platformsWe follow two rules throughout the report to keep comparisons fair: Compare like with like. Platforms where we have engagement rate data (LinkedIn, Facebook, Instagram, Threads, X, Pinterest, and sometimes TikTok, depending on available fields) can be compared to each other. Platforms where we use a proxy metric — Bluesky and Mastodon (total interactions) and YouTube (views) — shouldn't be ranked against engagement-rate platforms as if they're measuring the same thing.Treat each platform's metric as a within-platform benchmark. When we say a platform "leads," it means it leads within its own measurement lens — not that it's universally "better" than another platform using a different metric.What to keep in mind when reading this reportThese apply to every section unless we say otherwise. These are patterns rather than rules. Most findings are observational. We report patterns that are stable in the dataset without claiming they'll hold across every niche, account size, or team.Who's posting may have changed, too. Year-over-year movement can reflect platform changes and shifts in who's posting — adoption patterns, account mix, industry mix, and maturity. We can't always separate the two.Platforms change constantly. Features, ranking systems, and UI surfaces evolve all the time. Our findings describe how content performed in the window we measured, not how it will perform forever.Not all engagement is the same behavior. A like, a save, a repost, and a reply carry very different intent. They're counted as "engagement" where the platform defines them that way, but they're not interchangeable — and we try not to treat them as if they are.How each study worksThis report combines multiple analyses. Each one uses a method matched to the question it's trying to answer. Cross-platform baseline and year-over-year comparisons Goal: Establish "typical" engagement by platform and how it's moved.Metric: Median engagement rate where available; otherwise, the strongest proxy metric (views, reach, or interactions).Output: Platform tiering, monthly trend lines, year-over-year deltas.Reply effect analysis Goal: Measure whether replying to comments is associated with higher engagement within the same account.Method: Within-account modeling (as described above), comparing each account to itself over time, controlling for stable differences between accounts and relevant covariates (account size, niche, location, where available).Output: Estimated engagement lift when replies are present, by platform.Content format performance by platform Goal: Identify which formats generate higher typical engagement within each platform.Metric: Median engagement metric per post by format — engagement rate for most platforms, engagement as a percentage of reach for Instagram, total interactions for Bluesky and Mastodon, and views for YouTube.Output: Ranked format comparisons and relative deltas.Timing and frequency analysis Goal: Understand how publishing cadence and posting windows relate to performance.Frequency method: Compare median weekly posts for top performers vs. all users. Top performers are defined as the top 10% of total weekly engagement within each platform, each week.Timing method: Identify higher-performing time windows by platform, reported as windows rather than single "best time to post" slots.Output: Cadence and timing framed as amplifiers, not primary performance drivers.Posting frequency and follower growth Goal: Measure whether posting frequency is associated with follower growth within the same account over time.Method: Within-account modeling across 4.8 million channel-week observations from approximately 161,000 profiles on Facebook, Instagram, and X.Validation: Z-score analysis measuring each channel's weekly growth relative to its own baseline.Output: Evidence of a positive frequency–growth relationship, including a measurable "no-post penalty" (accounts that skip a week tend to underperform their own baseline growth rate).One last note on how we write about all of this: we state the metric first in every section — engagement rate, median engagement per post, reach, views, or interactions — so you always know what's being measured. We label the unit of analysis (post-level, account-level, or week-level). And we default to conservative language — "associated with," "tends to," "in this dataset" — unless the claim is strictly definitional. If we say something stronger, we've earned it in the data. The reality of engagement in 2026Engagement is not the same across platforms. The same account can publish similar content across platforms and see wildly different performance. That doesn't necessarily mean the content flopped — every network measures different actions, from different audiences, in very different feeds. With that in mind, let's take a look at the basics. Here's what "typical" engagement looks like on each platform and how it's shifted from previous years. (If you want the full breakdown of how we define engagement by platform, that's in the methodology.) Typical engagement in 2025Platforms cluster into clear tiers based on median engagement rate: Higher median engagement: LinkedIn (~6.2%), Facebook (~5.6%), Instagram (~5.46%)Mid-tier: TikTok (~4.6%), Pinterest (~4.0%), Threads (~3.6%)Lower median engagement: X (~2.5%)Most of the confusion around 'what's working' comes from ignoring these tiers. A post that generates a 4% engagement rate is underperforming on LinkedIn, but outperforming on X. How the baseline shifted: 2024 → 2025The only constant on social seems to be change. This applies to baseline engagement rates, too. Here's a look at how much these rates have shifted in just one year. Up: X: ~+44% (from ~2.0% to ~2.8%)*Pinterest: ~+23% (from ~3.2% to ~3.9%)Facebook: ~+11% (from ~5.0% to ~5.6%)Flat: TikTok: +~3% (from ~4.4% to ~4.5%)Down: LinkedIn: ~-5% (from ~6.4% to ~6.1%)Threads: ~-18% (from ~4.4% to ~3.6%)Instagram: ~-26% (from ~7.3% to ~5.4%)Important context: X's jump is the largest relative gain in the dataset, though X still sits at the bottom of the engagement-rate rankings. A big percentage jump from a low base. A note on what's driving these shifts: Year-over-year deltas can reflect real platform changes — algorithm updates, feature launches, UI redesigns — but they can also reflect changes in who's posting. In 2025, the number of posts we analyzed grew significantly across most platforms (often 2–3×). That strengthens our confidence in the 2025 medians, but it also means the underlying user base may have shifted. A growing mix of newer, smaller, or differently-niched accounts can move medians even if the platform itself didn't change in any meaningful way. We treat year-over-year movement as a directional signal, not a final verdict. Where engagement rate doesn't applyNot every platform in this report has a clean engagement rate. For some, we're working with a different primary metric entirely — which means they shouldn't be ranked against the engagement-rate platforms. YouTube Shorts: views. Median views more than tripled year over year (from ~86 in 2024 to ~268 in 2025). That sounds like a platform story, but it's likely at least partly a user-base story. As the mix of accounts publishing via Buffer shifts, typical view counts move even if the underlying distribution on YouTube is stable.Bluesky: interactions per post (likes + comments + reposts). The 2025 median sits around ~4 interactions per post, relatively stable month to month. Year over year, the median dipped slightly (from ~5 to ~4) while post volume nearly quadrupled — an expected pattern when usage broadens beyond early adopters.Mastodon: interactions per post (shares + favorites + comments). The median held steady at ~3 interactions per post through 2025, with no meaningful year-over-year change.With all of the above in mind, you're probably seeing how tricky it is to rank platforms based on engagement rate. It's not quite as cut-and-dried as "LinkedIn has the highest engagement rate. Even when metrics are similar, you're comparing apples with oranges. Views, interactions, and engagement rate are different metrics describing different things, and comparing them side by side is how you end up with misleading rankings. What we can say for sureEngagement isn't evenly distributed across platforms, and it doesn't mean the same thing everywhere. So what does tell you whether someone actually cares about your content — not just scrolled past it or tapped a like out of habit? That's where replies come in. The effect of replies on engagementWe've spent a lot of this report explaining how different all the major platforms are, but in this one area, we saw similar results across the board. The best part is, unlike many other factors on social, this is completely within the creator's control: replying to comments on your posts. Posts where the account replies to comments tend to earn more engagement than posts where they don't. These findings were similar across the six networks where we have reply data. Here, we used the fixed-effects approach to compare each account to its own performance over time, not to other accounts. The headline numbersAcross nearly 2 million posts from 220,000+ accounts on Threads, LinkedIn, Instagram, Facebook, X, and Bluesky, posts with replied-to comments consistently outperformed those without. Threads: +42% engagementThe largest lift in the dataset, and the Buffer team wasn't surprised to see Threads right at the top of the list. Threads gives replies unusual weight in both its UI and its ranking. At the profile level, about two-thirds of accounts performed better on posts where they replied. LinkedIn: +30% engagementWithin the same account, replying correlates with meaningfully stronger post-performance. LinkedIn also gives comments more in-feed weight than most other platforms, and now even has impression metrics for comments on posts. About 83% of profiles performed better when they replied — the highest rate of any platform in the dataset. Instagram: +21% engagementEven after controlling for whether posts had comments at all, replying correlates with higher engagement relative to the account's own baseline. About 63% of profiles performed better when they replied — a smaller share than LinkedIn, but notable on a platform where the feed is built around images and video, rather than conversation. Facebook +9.5% engagementOn Facebook, we measured reactions — likes, loves, hahas — to see the effect of replies on engagement, rather than total engagement. That means the lift doesn't come only from the replies themselves, which add to the comment count. But when an account replies to comments, the post gets more reactions from other people (possibly because it is surfaced more by the algorithm). The conversation seems to drive a separate, independent response from the wider audience. About 54% of pages performed better when they replied. On a platform this big and this mature, even a modest lift adds up to real volume. X: +8% engagementThis is the least certain result in the set — with smaller reply samples and X's tiered visibility mechanics, the data doesn't fully rule out noise. However, it's still statistically significant and directionally consistent with the other five platforms. Bluesky: +5% engagementThis is smallest lift in the set, from smaller samples on a newer platform. That said, it's still statistically significant and worth watching as the platform matures and reply behavior becomes more established. A little more contextThe platforms built around conversation, where replies get real visibility in the UI and the algorithm, are the ones where replying correlates most strongly with performance. Threads and LinkedIn are both designed for discussion, and their interfaces actually surface replies in ways most platforms don't. The lift from replies is still meaningful on Instagram and Facebook, just smaller. And it's weakest on X and Bluesky, where reply samples are smaller, and distribution is more unpredictable. It's also worth noting that the causal arrow could point in either direction. Strong posts attract more comments, which creates more opportunities to reply. And replying to comments drives engagement up, and that engagement drives replies, or so on. The differences in engagement by content type: A platform-by-platform breakdownWe've touched on this already, but it bears repeating here: what works on one platform might not on another. The platform-by-platform data that follows is where that gets specific. LinkedInLinkedIn has the highest median engagement rate of any platform in our dataset at ~6.1% in 2025. It dipped slightly from ~6.5% in 2024, but it's still comfortably on top. It's also a platform in the middle of an identity shift. LinkedIn has been courting creators, experimenting with a dedicated video feed and improved analytics. But it's carousels (document/PDF posts) that earn the most engagement on LinkedIn. Carousels earned a median engagement rate of 21.77%.Video came in at 7.35%.Images were close behind at 6.52%.Link posts at 3.81%.Text posts at 3.18%.There's a lot of variation within carousels, though. Among stronger-performing carousel posts, engagement was above 41%. Among weaker ones, it was around 5.4% — which is pretty close to the median rate for video and images. So even a below-average carousel is doing about as well as a typical video or image post. In an episode of Buffer Chat, LinkedIn's Head of Scaled Programs, Callie Schweitzer, emphasized video as a key priority for creators in 2025. Our theory is that LinkedIn might be headed down a similar behavior path as Instagram, where videos mean reach, but carousels mean engagement. More on this below. ThreadsThreads' median engagement rate came in at ~3.6% in 2025, down from ~4.4% in 2024 — an 18% decline that puts it closer to X (~2.5%) than to the higher-engagement platforms. Threads is positioned as a conversation-first space (or Instagram's text-forward sibling). But the formats need a bit more nuance than simply ranking them against each other. Video led with a median engagement rate of 5.55%.Images weren't far behind at 4.55%.Text posts came in at 2.79%.Link posts sat at 2.34%.There's a lot of variation within each format, though. A good text post can easily outperform a mediocre video. There's enough overlap across formats that any type of post can do well on Threads. Threads is still young and still refining its algorithms. We wouldn't be surprised to see shifts that change these numbers. But for now, mix in visuals with your Threads posts to give your posts a boost. InstagramInstagram's median engagement rate fell from ~7.3% in 2024 (the highest in the dataset that year) to ~5.4% in 2025 — a 26% decline that moved it from first place to third, behind LinkedIn and Facebook. When we look at engagement rate as a percentage of reach, carousels come out on top: Carousels led with a median engagement rate of 6.90%.Single images came in at 4.44%.Reels followed at 3.31%.Carousels earn roughly 109% more engagement per person reached than reels, and single images earn about 34% more than reels. Even static images comfortably outperform video when it comes to engagement on Instagram. However, as always, there’s nuance here. Reels, carousels, and single images serve different purposes. And there’s one thing worth noting: we're measuring engagement rate here — likes, comments, saves, and shares as a percentage of reach. But reels are often optimized for views rather than these kinds of interactions, so a lower engagement rate doesn't necessarily mean Reels aren't working. It may just mean people are consuming them differently. In addition, the format breakdown above doesn't capture the full picture, because reach and engagement point in different directions on Instagram. A separate analysis of 4M+ posts published via Buffer between January 2022 and October 2024 showed us that: Reels tend to get the most reach Reels vs carousels: 1.36× the reach (+36%)Reels vs single-image posts: 2.25× the reach (+125%)Instagram has a dedicated reels discovery tab, so reels have a built-in advantage for reaching people who don't already follow you — feed-native formats don't get that same boost. Carousels tend to get the most engagement Carousels vs reels: 2.09× the engagement rate (+109%)Carousels vs single-image posts: 1.55× the engagement rate (+55%)Single images vs reels: 1.34× the engagement rate (+34%)Carousels keep people on the post longer, meaning more chances to save, share, and comment, and potentially multiple chances to reappear in-feed. It's a bit like Instagram is two different platforms in one, depending on where you post your content. And which 'platform' you choose depends on the goal of your content. Here's a helpful way to look at it: Discovery mode (reaching new people): Reels are more likely to reach people who don't follow you.Relationship mode (engaging your existing audience): Carousels drive deeper interactions from people who already do.The "best format on Instagram" has no single answer as it depends on your goals. FacebookFacebook's median engagement rate rose to ~5.6% in 2025 (up from ~5.0% in 2024, a +11% gain), making it the second-highest engagement platform behind LinkedIn and one of only three where engagement moved meaningfully upward year over year. But on Facebook, the gaps between formats are small. Images led with a median engagement rate of 5.20%.Video at 4.84%.Text posts at 4.76%.Link posts at 4.43%.That's less than one percentage point separating images from text. On Facebook, format choice matters less than almost anywhere else in this dataset. Images have a slight edge, and link posts are slightly behind (which is consistent with the broader trend of platforms keeping users on-platform). But the differences are small enough that what you post about probably matters more than whether it's a photo or a video. X/TwitterX's median engagement rate jumped from ~2.0% in 2024 to ~2.8% in 2025 — a +44% increase, the largest relative gain in the dataset. But X still sits at the bottom of the engagement-rate platforms, and the bigger story is structural. X introduced Premium accounts in March 2023, promising several new features for paid users, with better content performance among them. In our research into the effect of X Premium on reach and engagement, we started seeing that happen around January 2025. Before that, Premium and regular accounts moved in similar directions on engagement rate. After January 2025, they split — Premium engagement rates rose while regular account engagement rates fell. But Premium divide aside, text still wins on X by a wide margin: Text posts led with a median engagement rate of 3.56%.Images at 3.40%.Video at 2.96%.Link posts at 2.25%.Text and images are close enough that both work well. Video can work on X, but it doesn't carry the same default advantage here as on other platforms. TikTokTikTok's median engagement rate came in at ~4.5% in 2025, roughly flat from ~4.4% in 2024. It sits in the middle of the pack — behind LinkedIn, Facebook, and Instagram, but ahead of Pinterest, Threads, and X. The format finding here probably won't surprise anyone: on a video-first platform, video performs best. Video led with a median engagement rate of 3.39%.Images at 1.92%.What is interesting is how competitive images have become. TikTok started as a pure video platform, but with the introduction of carousels and photo posts, images are proving more viable than you might expect. BlueskyBluesky uses total interactions (likes + comments + reposts) rather than engagement rate, so it's not one-to-one with the other platforms in this section. Video earned a median of 5 interactions per post.Images at 4.Links at 3.Text at 3.The median dipped slightly year over year (from ~5 to ~4) while post volume nearly quadrupled. This is to be expected as a platform grows beyond its early adopters and the user base broadens toward smaller and newer accounts. A brief look at some other platformsNot every platform in our dataset got its own deep dive in the web report. For these platforms, the data we have is solid enough to share what we have, but not enough for the full treatment we gave those above. Here’s where things stand on Pinterest, YouTube, and Mastodon. PinterestPinterest's median engagement rate rose to ~3.9% in 2025, up from ~3.2% in 2024 — a +23% gain that makes it one of only three platforms where engagement moved meaningfully upward, alongside X and Facebook. Video is the clear winner on Pinterest. Video led with a median engagement rate of 5.75%.Images at 3.15%.That's nearly double the engagement for video — one of the largest format gaps in the dataset. Pinterest has been investing in video features, and the data suggests that investment is paying off. If you're still treating Pinterest as an image-only platform, consider adding videos to your strategy. YouTubeFor YouTube, we measure median views rather than engagement rate, which makes it difficult to compare directly with rate-based platforms. (See The Baseline section for full context on why.) The median YouTube video published through Buffer earned a median of 433 views (52 views on the lower end and 1,224 on the higher end). However, this data more likely reflects shifts in who's publishing via Buffer at least as much as YouTube's underlying distribution — as the mix of accounts changes, typical view counts shift even if the platform itself is stable. The main thing to note here is: views are the first gate to pass on YouTube. Likes, comments, subscriptions, and shares are often sparse relative to view volume. A strong median view count can still come with very low interactions. MastodonMastodon uses total interactions (shares + favorites + comments) instead of engagement rate, and is the most stable platform in the dataset. Images and video both earned a median of 3 interactions per post.Links and text both at 2 interactions.Timing and frequency can boost engagement –– but not drive it“How often should I post?” and “When should I post?” are two of the most common questions creators and teams ask us at Buffer. The honest answer is there isn't a single universal number for either one. But there are clear patterns in the data. THere’s how to think about both: timing and frequency are amplifiers. They increase your chances of success and concentrate it into higher-probability windows. But they don't create engagement on their own: they boost what's already working. On platforms where virality plays a bigger role in whose posts get seen — TikTok is the best example — there's another factor to consider: posting more also increases the odds of any single post breaking out. In that context, frequency isn't just an amplifier; it's also a numbers game. One thing worth noting up front: frequency and performance tend to travel together, and there are a few possible reasons: Resources. Successful accounts can afford more output thanks to bigger teams, better workflows, and more assets.Momentum. Higher engagement motivates more posting. The causal arrow runs in both directions.Platform fit. Some platforms may reward frequent publishing more than others, but the strength of that effect varies by audience and format.We can't fully separate these in observational data. But what we can do is show you what the patterns look like. Frequency: top performers post more, more consistentlyWe compared weekly posting frequency between two groups on each platform: the median account and the top 10% by total weekly engagement. To qualify, accounts needed at least 10 posts in the past year, in at least 4 different weeks. Across platforms, top-performing accounts post more frequently than the median user — and they do it consistently across the year, not just during spikes. The gap is widest on text-forward platforms: X, LinkedIn, and Threads. These are feed-dense environments where it takes less production effort to post, so top performers pull ahead more clearly by posting more often. The gap is closer on visual-heavy platforms, especially Instagram and TikTok. Top performers still tend to post more, but the difference is less consistent — probably because these formats take more effort to create, so it's harder to maintain a high volume. The no-post penaltyThis is the finding that surprised us most in the frequency data. In a separate analysis of 4.8 million channel-week observations from ~161,000 profiles on Facebook, Instagram, and X, we measured how follower growth changes when the same account posts at different frequencies across weeks (see Methodology section for details). The pattern was clear: accounts that didn't post in a given week consistently underperformed their own baseline growth rates. We call this the "no-post penalty." Even posting just 1–2 times per week produced a meaningful improvement over weeks with no posts at all. And the benefits continued to scale. Accounts posting 10+ times per week saw the largest gains, averaging 32 additional followers per week compared to silent weeks. But the most important threshold is the first one: any posting is substantially better than no posting. Consistency matters more than volume. There's a tension here, though: while posting more is associated with higher total engagement and follower growth, our engagement rate analysis of 15.7M posts suggests that reach per post tends to decline at higher frequencies. Posting more helps you grow in aggregate, but each individual post may reach a smaller share of your audience. The best approach is a cadence you can sustain while protecting quality — not maximum volume at the expense of everything else. What we can and can’t claimOne thing we can say clearly from this data: top performers publish more often than the median account, across platforms. What we can't say is that a single "optimal" frequency exists across niches, account sizes, or teams — or that posting more causes higher engagement. How timing fits (and why it’s not the 'secret sauce')From our timing analysis, two things are consistently true: There’s no universal “best time to post” across platforms. Each network has its own usage rhythms.The “best time” is usually a window, not a single slot. High-performing posts tend to cluster in certain parts of the day and week, but the difference between top time blocks is often smaller than people expect.Timing is a distribution advantage or an amplifier. It can help a good post get its first push but it can’t turn an average post into a high performer. The windows below are where higher-performing posts clustered in our data. Use them as starting points for testing, not rules: Facebook: 8–11 a.m. weekdays, peaking Thursday at 9 a.m.Instagram: 6–9 p.m. weekdays, peaking Thursday at 9 a.m.LinkedIn: 3 p.m.–8 p.m. weekdays, peaking on Wednesday at 4 p.m.TikTok: 8 a.m.–11 a.m. weekends, peaking Sunday at 9 a.m.X: 6–11 a.m. weekdays, peaking Tuesday at 9 a.m.Threads: 6–11 a.m. weekdays, peaking Thursday at 9 a.m.Bluesky: 6-9 p.m. weekends, peaking Sunday at 5 p.m.The data suggests a pretty clear pecking order: what you post matters most, how often you post matters a lot, and when you post matters least. That's not to say timing is irrelevant — but the biggest gap in this data isn't between "good timing" and "bad timing." It's between posting and not posting. So experiment with timing to find what works for your audience, but don't let the search for a perfect schedule keep you from hitting publish. What this meansWe set out to document how engagement is actually functioning across platforms — not to tell readers what to do. But after analyzing tens of millions of posts, a few things stand out. We kept looking for a sophisticated answer to engagement in 2026, but the data kept giving us the simple one. The strongest signal in this entire dataset wasn't a format trick, a timing hack, or an algorithm exploit. It was replies. On every platform we studied, creators who reply to comments do better than creators who don't. It's maybe the simplest possible version of what social media was supposed to be: people talking to the people who talk to them. The next thing the data kept saying: show up. The biggest gap in the frequency data isn't between good timing and bad timing. It's between posting and not posting. The no-post penalty was real and consistent across all platforms. So show up first, optimize second. And the third takeaway: fragmentation is real, but it's not bad news. Every platform defines engagement differently, measures it differently, and rewards different behaviors. There’s no single playbook to copy — which means there's no single algorithm to lose to, either. Growth can happen anywhere, on any platform, as long as the work is good and you're showing up. It's also worth noting that the platforms where reply effects were strongest — Threads and Bluesky — are also the newest. They were built in an era where the value of conversation is understood differently than when Facebook and X first launched. We can't prove social media is shifting toward conversation over reach. But the platforms being built right now are designed as if it is — and the engagement data from those platforms looks like that bet is paying off. Whether or not that's a trend, the practical takeaway is the same: reply to the people who engage with you, post consistently and make good content. View the full article

BYD just destroyed any remaining argument against electric vehicle adoption. At a March 5 launch event in Shenzhen, China, it announced the Blade Battery 2.0, a new battery that can drive more than 621 miles on a single charge. In the process, the company has exposed just how far behind the rest of the electric vehicle industry has fallen. Gasoline cars have held onto two supreme advantages for a century: the five-minute pit stop and the typical 400-mile range that enabled people to take long road trips without worry. Meanwhile, EVs have suffered from long charging times and short ranges that induced range anxiety in potential buyers, who mostly preferred to stay with internal combustion engine cars or hybrids. With the release of its new Blade Battery 2.0 and Megawatt Flash Charge 2.0 architectures, the fear is over. According to the official figures announced in the event, high-volume production BYD cars like its new Denza Z9GT now can drive over 621 miles on a single charge, add roughly 250 miles of range in the time it takes to order a coffee, and rely on a battery pack that refuses to die before the car does, with a guaranteed 620,000-mile lifetime unheard of in any EV. BYD’s latest battery and charging tech makes current other electric vehicles look like Model Ts— at least for now. As the second largest manufacturer of batteries in the world, BYD is currently the batteries to other manufacturers like Toyota, Kia, Hyundai, and even Tesla itself. BYD’s new charging architecture kills the ICE pitstop advantage entirely by pushing 1,500 kilowatts of peak power through a single cable, or up to 2,100 kilowatts if using a dual-gun setup. To understand the sheer power of that electrical flow, you have to look at the current industry standard. Think of kilowatts as the width of a water pipe filling a swimming pool. A standard home charger trickles power overnight at roughly 7 kilowatts, like a garden hose. A Tesla Supercharger—long considered the gold standard of public fast-charging—maxes out around 250 kilowatts. BYD is unleashing six times that amount of energy, effectively hooking the car up to a high-pressure municipal water main. During a live demonstration on stage, BYD plugged in its new Han L sedan, making the battery jump from 10% to 80% capacity in exactly 6 minutes and 30 seconds. On the keynote screen, BYD officially declared a charging speed of “1 second = 2 kilometers.” Translated to real-world driving terms, five minutes plugged into this hardware yields between 250 and 310 miles of driving range. Of course, a 1,500-kilowatt charger is useless without a network to plug into. To solve this, BYD confirmed it is rolling out 15,000 of these megawatt charging stations across China by the end of 2026. The company is building over 4,000 of these stations independently, while deploying the rest through joint ventures. They also plan to deploy a European 3,000-charger network by the end of 2026. Anxiety no more The Blade Battery 2.0 pushes the driving range of upcoming vehicles like the Yangwang U7 past the 621-mile mark, easily beating a standard full tank of gas, which usually taps out around 350 to 400 miles for sedans (although a handful of diesel, hybrids, and gasoline models with oversized tanks can go beyond 600 miles). BYD achieved this through a massive leap in energy density, a measure of how much raw electrical energy you can pack into a given physical weight. For years, the auto industry faced a rigid dilemma. You could build a battery using Lithium Iron Phosphate (LFP) chemistry, which is cheap, highly durable, and extremely safe, but the industry standard density hovers at a mediocre 150 to 180 watt-hours per kilogram. The alternate Nickel Cobalt Manganese (NCM) chemistry, which typically packs 200 to 280 watt-hours per kilogram but is more expensive and prone to catching fire. Because of their architecture and chemistry, NCM batteries have low abuse tolerance and release a lot of oxygen when punctured during an accident, which feeds the battery fire and make it virtually impossible to put out. LFP batteries are much harder to puncture and, if it happens, they release minimal oxygen. The density boost comes from Blade Battery 2.0 new internal structure. First, BYD engineers ground the LFP battery’s chemical materials into an ultra-fine microscopic powder to cram vastly more raw energy into the exact same physical space. Second, they built shorter, direct internal superhighways for the electrical charge, allowing the battery to absorb massive amounts of power in seconds without overheating. This increased the energy density of the new version by 36% to 40% over its previous generation. The new packs hit between 190 and 210 watt-hours per kilogram and, they say, for a lower cost (they didn’t disclose the cost, but BYD claims it will boost their profit margins). Effectively, the Chinese manufacturer has delivered the promises Elon Musk made back in 2020, when he introduced the idea of his so-called “revolutionary 4680 battery cell” that would dramatically increase range and slash costs. Half a decade later, Tesla’s 4680 rollout has been plagued by manufacturing bottlenecks and underwhelming density figures. Tesla was forced to buy BYD’s first-generation Blade batteries to power the Model Y built in its Berlin gigafactory while using its failed 4680 in some Model Ys at its Texas factory. The Cybertruck uses an improved version of the 4680 called Cybercell, which reportedly has a 272 Wh/kg density. It gets better Another big selling point of new battery technology is its lifetime. Batteries represent roughly 30 to 40% of the cost of EVs, so consumers naturally fear the day their battery degrades to the point of a ruinously expensive replacement. Right now, the industry average electric vehicle battery lasts roughly 150,000 to 300,000 miles. The standard NCM batteries used by most competitors tap out after 1,000 to 2,000 charge cycles before losing a severe percentage of their capacity and needing a swap. The Blade 2.0 is rated for over 5,000 charge cycles. While multiplying those cycles by the maximum range yields a theoretical limit in the millions, BYD officially rates the degradation curve to guarantee an operational lifespan of 1.2 million kilometers, or roughly 745,000 miles. The average American drives about 13,500 miles a year. At that pace, you would have to drive this car every day for 55 years before hitting the end of the battery’s life. The battery will outlast the metal chassis, the seats, and probably the driver. You would assume these specifications come with a brutal premium, but the financial mechanics here are moving in reverse. BYD managed to lower the production cost of the Blade 2.0 by 15% to 30% compared to its previous generation. While the previous Blade was mostly hoarded by six-figure luxury vehicles, now the Chinese company claims the new batteries and charging architectures are going into high-volume, mainstream 2026 models like the Tang and the Song, which sit in the $19,000 to $30,000 price bracket. It’s not perfect, however. There’s still one undeniable advantage for the internal combustion engine: bad winters. LFP batteries historically hate freezing temperatures. A gas tank holds the exact same amount of combustible energy at negative 4 degrees Fahrenheit as it does at room temperature. An EV battery, however, usually loses 10% to 20% of its range to heat the cabin, and its chemical reactions slow down so much that fast-charging becomes impossible until the pack warms up. BYD integrated an internal pulse-heating system and a full liquid thermal management array directly into the Blade 2.0 to avoid losing so much energy and allow for fast charging in extremely cold environments. At -4°F, the Blade 2.0 retains over 85% of its capacity. At -22°F, it keeps 80% of its capacity (previous-generation LFP electric vehicles could drop as much as 50% at this temperature). Standard Nickel Cobalt Manganese (NCM) EV batteries typically retain 70% to 80% of their total capacity at -4°F, falling to roughly 40% to 60% at -22°F. EVs with the standard NCM batteries also actively restrict or entirely lock out fast-charging at low temperatures to prevent permanent physical damage to the battery cells. But according to the company’s CEO Wang Chuanfu during the event, “the new Blade Battery can be charged from 20% to 97% in less than 12 minutes in temperatures as low as -4°F, enabling a driving range of 483 miles.” That, while not matching the 0% loss of gasoline, is an impressive claim too. We will have to wait for test drives to see how all these claims pan out. But, judging by how well the previous generation worked, I have no reason to doubt it. Add the fact that all this tech will be available across the BYD entire car range from the luxurious new Yangwang U7 sedan to the budget Dolphin, and apparently we may have entered a new era for electric vehicles. Too bad it will not be arriving in the U.S. anytime soon. View the full article

AI has changed the way people shop. 58% of consumers now use GenAI tools instead of traditional search to find products. Imagine your customer runs a simple query in Google’s AI Mode: “Winter jackets for women.” Instead of a long list of links, they get direct product recommendations — alongside: Descriptions of features and best use cases Ratings and reviews Editorial sites that mention the product Direct comparisons with top competitors All in one response. Which raises an obvious question: Why do some products show up, while others are ignored entirely? Many factors influence AI recommendations. But one of the most important — and most controllable — is your product pages. In basic terms, AI needs to understand what your product is and who it’s for. When that information is clear, structured, and specific, your products have a much better chance of appearing in AI results. In this guide, we’ll break down how AI evaluates product pages, and which elements matter most. Plus, we’ll see how leading ecommerce brands structure their pages to get recommended. Free checklist: To get a head start, download our Product Page AI Optimization Checklist. It includes everything you need to get more product mentions in AI platforms. How AI Models “Think” About Product Pages Ever wondered how large language models (LLMs) choose which products to surface in answers? While there’s a lot at play, you can basically narrow it down to two factors: Consistency: Information about your brand and products matches across your website and third-party sites Consensus: Multiple reputable sources validate your product’s quality, use cases, and performance. This includes reviews on your product pages and third-party sites. For LLMs to confidently cite a product page, they need consistent, up-to-date information. AI models analyze product pages to pull details that help them answer user queries. Remember, AI queries don’t look like a regular search. Prompts are often highly specific requests for products that fit a clear use case or situation. Example: What are the best women’s road racing shoes for a 10K in Ireland? AI looks for product pages that clearly communicate: What the product is What it’s used for Who uses it In what situations it can be used This helps the system understand your product in the context of user queries. Take this Nike road racing shoe product page, for example. AI systems understand when and how to recommend this product because it contains details like: What the product is: “Women’s Road Racing Shoes” Who should use it and when: Racing-related language like “marathon” and “race day shoe” makes it clear this product is for racing When I searched “best road racing shoes for women” in AI Mode, it recommended Nike’s Alphafly. And where did the information it quoted come from? Nike’s own product page. AI models also look for consensus signals on product pages. This includes customer reviews and ratings. When AI analyzes reviews, it looks for patterns. This includes repeated mentions of specific use cases, features, or product benefits. For example, the Nike Alphafly is highly rated with plenty of reviews on the Nike website. Among other benefits, this improves its chances of being recommended by AI platforms. But AI doesn’t rely solely on product pages. It cross-references independent sources to back up claims about your products. In a similar search for racing shoes, I found that AI Mode cites various third-party sources to support its recommendations. Like this one, that includes a review of Nike shoes, complete with product details. Product pages are one piece of the AI visibility puzzle. But they create the foundation AI systems need to confidently recommend your products. Further reading: Learn how LLMs recommend brands in Semrush’s AI Visibility Index. 6 Essential Elements of a Product Page for AI Visibility You likely already have some (or all) of the elements below on your product pages. But for AI visibility, having them isn’t enough. What matters is clarity, specificity, and structure. Note: These elements aren’t in any particular order: all are important for AI visibility. 1. Clear Product Descriptions with Semantic Language A clear product description explains more than what your product is. It spells out what it does, who it’s for, and why someone would choose it. This matters for AI visibility because LLMs rely heavily on semantic retrieval. In other words, AI understands the intent and meaning behind queries. Not just exact-match keywords. For example, when someone searches for “vacuum for pet hair,” AI doesn’t just look for that phrase. It also looks for semantically related terms. Things like “stubborn hair,” “carpets,” “pet odors,” and “allergens.” These terms help AI infer use cases, surface the right features, and decide when your product is a good fit. Including them on product pages improves your chances of appearing in AI-generated answers. So, how do you find these terms? First, read forums, reviews, and social media conversations. Learn how people talk about the problems they’re facing and the products they’re using. Using our vacuum example, I dove into r/VacuumCleaners. There, I found recurring phrases around weight, clogging, tangles, and flooring-specific concerns. Next, conduct keyword research on related terms. This shows you how people actually phrase their searches. A tool like Semrush’s Keyword Magic Tool is great for this task. Note: A free Semrush account gives you 10 searches in the Keyword Magic Tool per day. Or you can use this link to access a 14-day trial on a Semrush Pro subscription. Enter a keyword, such as “pet hair vacuum.” The tool will return a list of “Broad Match” queries, which contain variations of your keyword. Review the “All Keywords” list on the left to find common themes. Then, check the monthly search volume for each term. In our example, we might use “handheld,” “carpet,” and “hardwood” as semantic keywords. Collect a few key terms, and use them in product descriptions to explain what your product does. You can still be creative. Just don’t sacrifice clarity. Here’s what this looks like in practice. I asked AI Mode for the best lightweight vacuum for pet hair. One of the top recommendations was a Shark vacuum. User preferences and personal context aside, AI Mode recommended this product for a few reasons: For one, it has strong consensus signals from third-party reviews and editorial sites. (Which you can see from the sources on the right side.) But let’s also take a closer look at the product page. The product name alone — Shark UltraLight PetPro Corded Stick Vacuum — gives a core use case. It’s meant for lightweight, pet-focused cleaning. The product description reinforces that message with simple, specific language: Captures stubborn hair Works on carpets and floors Hand vac option Weighs less than three pounds That same phrasing shows up in the AI response. This strongly suggests AI Mode is pulling this information directly from Shark’s product description for this vacuum. Bottom line: Customer-focused, use-case-driven language helps AI understand when to recommend your product. This increases your chances of appearing in AI search results. Further reading: Need inspiration? Check out some of our favorite ecommerce website examples. 2. Pricing and Availability in Real-Time Feeds LLMs read product data from two places: your product pages and merchant feeds. If your site has accurate structured data, AI can use that. But crawlers don’t run every minute. That means prices and stock can be stale. That’s where a live product feed or API comes in. This includes Shopify’s Catalog API, OpenAI’s Product Feed Spec, and feeds submitted through Google’s Merchant Center. Pro tip: OpenAI product feed submission is currently available only to approved partners. Fill out the Merchant Application form for consideration. When you use these, AI search engines can fetch current prices and inventory on demand. That’s the tech that powers real-time recommendations and in-chat shopping in ChatGPT and other AI platforms. More platforms are also adding this capability. Google is rolling out a Universal Commerce Protocol. This feature brings buy-in-chat functionality to eligible product recommendations in AI Mode and Gemini. But what if you don’t use a product feed or API? LLMs can still find product information on public webpages. But it may be outdated. And that’s a problem. AI platforms evaluate recency and consistency. Mismatched prices or outdated stock can hurt your AI visibility. In part, because it leads to a poor customer experience. ​​To see how this plays out in practice, I tested ChatGPT’s “Shopping research” mode. The AI asks questions to narrow results, including how much you want to spend. I told ChatGPT I was looking for a new couch. I specified both my budget and need for delivery to Massachusetts. ChatGPT returned five options, all of which fit my budget and availability requirements. The “Best overall” option even highlighted that it was “in stock for fast delivery” to my state. To further test how price affects results, I asked if any of the recommended couches were on sale. It narrowed down my options and provided sale pricing. ChatGPT only mentioned one couch as being on sale. To find out why, I reviewed the product pages for each recommendation. But only one clearly highlighted both the original and sale price. Walmart’s product pages boldly showcase the previous price versus the discount. In its response, ChatGPT specifically mentioned that Walmart displays this info on its product page. Walmart also submits its product feeds to platforms like Google Merchant Center. So its pricing (both sale and original) is clear and current across platforms. Product feeds and APIs keep your price and inventory fresh. When AI systems have access to this data, they can recommend your products when users narrow options by price, availability, or discounts. 3. Ratings and Reviews Many AI systems display ratings and reviews in product recommendations. In AI Mode, you can click a product recommendation and see reviews directly in the sidebar. ChatGPT also includes information from reviews. It often surfaces them as part of the response: But LLMs do more than show you reviews. They also weigh reviews and ratings when choosing recommendations. ChatGPT often includes labels like “Budget-friendly” or “Most popular” based on reviews. OpenAI has confirmed that answers may include summaries of the themes most commonly mentioned in reviews. That could mean pros, cons, and use cases pulled directly from reviews. Here’s how that looks in practice when I search for warm winter hiking boots: Ultimately, reviews on your product page don’t just affect whether your product appears in AI search. They can also influence how it’s positioned. When AI systems analyze reviews, they look for consistency: Repeated mentions of specific use cases Commonly praised features Patterns in star ratings Shared language around benefits or problems The more clearly those patterns emerge, the easier it is for AI to confidently recommend — and describe — your product. This applies to reviews on your own product pages and on third-party sites. When I asked AI Mode for a hydrating cleanser for sensitive skin, the first recommendation was a product from CeraVe. Interestingly, the product description itself doesn’t explicitly emphasize “sensitive skin.” But the reviews on CeraVe’s product page do. Here’s what I noticed: Reviews are tagged with commonly mentioned phrases One of the most prominent tags is “sensitive skin” There are over 100 reviews referencing sensitive skin — most of them positive Having reviews on every product page is a best practice that increases trust and authority. Encourage customers to leave detailed feedback by: Prompting for use cases in review forms Asking follow-up questions after purchase Offering light incentives (like a coupon) in exchange for honest reviews Note: The most important thing is that these reviews are real. Fake or AI-generated reviews may temporarily improve your brand’s visibility in AI search. But they are never worth the long-term risk to your reputation. 4. Contextual Use Cases AI search looks for explicit connections between what a product is and why someone needs it. So, your entire product page should explain when, why, and in what situations a product makes sense. This requires a shift in how you think about product marketing. Instead of asking, “What can this product do?” Ask, “In what specific scenario would someone actively look for this?” Start by identifying who buys your product and what triggers that purchase. If you don’t already have this insight, customer interviews are your fastest path. Look for: The situation that prompted the search The alternatives they considered The constraint that mattered most (travel, space, safety, performance, etc.) Once you have this, choose one or two clear, specific use cases to feature on each product page. Don’t just list all the possible ways your product can be used. AI isn’t great at matching vague versatility. Instead, focus on the use cases that come up repeatedly in customer conversations. That way, AI can match your product to a specific intent. Let’s look at an example for an electronics brand. This product page for Anker’s 3-in-1 mobile charger states it’s “ultra compact and travel friendly.” When I search for travel-friendly chargers on ChatGPT, Anker’s 3-in-1 device is the top recommended product. Obviously, this little charger is a great option for more than just travel. But by calling out that use case on the product page, it makes it easier for LLMs to recommend it in related queries. 5. Awards and Certifications LLMs prioritize trustworthy, verifiable information when recommending products. One of the strongest ways to demonstrate that trust is to feature third-party validation on your product pages. This includes: Industry awards and “best of” recognitions Third-party testing results Safety and quality certifications Sustainability or ethical production badges To see how much awards affect AI visibility, I analyzed 50 ecommerce brands in Semrush’s AI Visibility Overview tool. This included Samsung, Patagonia, Everlane, Caraway, and others. First, I identified brands with high AI Visibility scores. This is a Semrush metric that measures how often brands appear in AI-generated answers. I focused on brands scoring above their industry average. (This varies by industry, but is generally between 60 to 90.) Next, I looked at how many of the top-ranking brands feature awards and certifications on their product pages. And I found something very interesting: 82% of the brands with medium to high AI visibility prominently feature awards and certifications on their product pages. For example, Samsung has an AI Visibility score of 90. And its product pages feature multiple awards. Like being “rated #1 in camera quality” by the American Customer Satisfaction Index. And winning “Best Phone Camera” by Consumer Reports: When I asked Claude which phone has the best camera quality, the Samsung Galaxy was one of its top recommendations: BabyBjorn has an AI Visibility score of 67. A quick look at its product pages reveals certificates and awards on every product page. Like this one that references a “Best Bouncer” award from Parents Magazine: When I asked ChatGPT to recommend the “best and safest baby bouncer,” BabyBjorn was the #1 pick: Now, this is correlation, not necessarily causation. And awards and certifications are not the only factor. But they can make a difference for product page visibility in LLM search. If you already have awards and certifications, showcase them prominently on your product pages. If you don’t, create a strategy to earn them. Target industry-specific certifications (safety, quality, sustainability) and awards from reputable organizations. This includes relevant certifications and “best” awards through PR outreach. 6. Structured Attributes and Schema Markup Structured attributes are pieces of product information that machines can easily understand. This includes things like: Price Dimensions Materials Ratings Availability Color Size Warranty details These attributes are vital components of a product page. Use tables, bullet lists, or specification sections to clearly structure them for machines and customers. They should also be in your structured data and product feeds. For example, health company Vitamix features a “Specifications” section on its product pages: We can’t say definitively that schema affects LLM visibility (yet). But major AI search engines confirm they rely on structured attributes to understand and recommend products. What OpenAI says: “When determining which products to surface, ChatGPT considers structured metadata from first-party and third-party providers (e.g., price, product description). Depending on your needs, some of these factors will be more relevant than others. For example, if you specify a budget of $30, ChatGPT will focus more on price, whereas if price isn’t mentioned, it may focus on other aspects instead.” It’s also still a best practice for traditional SEO. Plus, it’s no secret that structured data helps products appear on Google’s main page and Shopping tab. It’s what allows users to refine results, see ratings, and check prices right on the first page of Google. But here’s where it gets interesting. When I conducted a search in AI Mode, Google’s own shopping cards were the main sources. Clicking into one of those sources, I saw even more of that search-friendly structured data. And where does all this information come from? You guessed it: the original product page. That same structure is what enables Google’s AI responses to display live pricing, availability, sales, and comparisons. Clear, consistent schema simply gives search engines and LLMs more to work with. That context helps AI more confidently recommend your product in related queries. AI Visibility Essentials for Product Pages (By Industry) The elements above matter on every product page. But AI evaluates product pages differently depending on the category. In this section, we’ll break down the category-specific product page details that AI looks for across six common ecommerce industries. Fashion Brands Ask any AI engine for clothing recommendations, and you’ll notice something consistent: the results highlight fit, materials, and comfort. Clearly, the most important product page elements for fashion brands are: Clear sizing and conversion charts Material and care information Customer fit data Sustainability certifications and ethical production badges Fashion queries are also highly specific to the individual shopper. To see how AI handles these searches, I used Semrush’s AI Visibility Toolkit. I analyzed the topic “jeans for women” using Semrush’s Prompt Research tool. What’s revealing is the variety of queries under this topic. Take “Plus size and curvy women’s jeans” for example. Even within this niche, searches vary widely: “Best plus size jeans for big thighs” “Best curvy fit jeans” Most comfortable jeans for curvy women” Across all these queries, the AI responses consistently emphasize the same details: High-rise styles Stretch denim Tummy control Specific silhouettes like bootcut These details are pulled directly from product pages and customer reviews: For AI to match products to these specific queries, it needs structured details on your pages. This is something Abercrombie & Fitch does well. They display clear fit guidance and aggregated customer fit feedback prominently on product pages. Health and Wellness Products Nothing is more important to health and wellness brands than trust and safety. That’s why non-negotiables for product pages in this industry include: Full ingredient composition Clear dosage and instructions Contraindications and allergen warnings Source transparency Clinical studies or certifications Searches for health products are often deeply personal and complex. Many start with a product type and the demographic it’s best for. For example, the topic of “infant multivitamins” includes these common searches: “Where can I buy reliable infant multivitamins?” “How do I choose the best multivitamin for my baby?” In their responses, AI models pull from ingredient lists, dosage information, and certifications. Brands that perform well for wellness-related AI queries follow the same pattern. They provide detailed information about ingredients, sourcing, and production on their product pages. This is what helps popular health company Thorne get recommended often in AI search results: Their product pages list ingredients in detail: They also include dosage instructions and verifications of the product quality. All in a clear, machine-readable format. Electronics When it comes to electronics, AI loves to quote specs. Battery life, screen resolution, charging speed, refresh rates, and more are all pulled into responses. So every electronics product page should include the essentials: Full technical specs Compatibility information Setup or installation guides Safety and efficiency certifications For example, even a simple search — “best cameras for night photography” — returns spec-heavy recommendations. Structured specs give AI systems what they need to compare products. This is important on your own site and third parties. Brands like Sony excel here. They ensure their product and retailer pages feature technical details that are consistent and in-depth across platforms. Home and Furniture Brands Furniture shopping comes with one big question: Will it fit? AI knows this, which is why technical details dominate recommendations. Your home and furniture product pages need: Clear dimensions and room size recommendations Assembly requirements (tools, time, difficulty) Materials and care details Quality and sustainability certifications For example, in a search for modular sofas for small apartments, ChatGPT mentions configurations in its answer: One of its top recommendations is a couch by home brand Burrow. While many factors go into this, its product page is definitely one of them. It features different configurations of their modular sofas. Plus, the dimensions of each. It also contains other vital information that users might ask AI systems, such as detailed materials and fabric care. Outdoor and Sports Equipment Customers need to know whether your products will survive their outdoor adventures. Which is why AI takes these elements into account: Weather ratings and technical materials Performance specs (capacity, weight, range) Use-case scenarios Safety certifications or features Let’s say your customers ask about hiking backpacks. They’ll see AI models highlight key features, max load, and materials. Osprey’s backpacks are regularly recommended by AI. This is because they clearly state use cases like “week-long backpacking trips”: They also include features that make it ideal for common use cases: materials, weight, volume, dimensions, and load range. Baby Products Baby products trigger some of the most safety-sensitive AI recommendations. AI models look for structured, verifiable details when recommending anything for infants. If you sell baby products, here’s what your product page should include: Age and weight suitability Safety certifications (like OEKO-TEX, GREENGUARD) Ergonomic or developmental benefits Material and care instructions For example, BabyBjorn includes safety certifications on its product pages. And goes deep into safety information. This includes how the fabrics are developed, and the appropriate age and weight for safe use. When I asked Perplexity for the safest baby carrier on the market for newborns, BabyBjorn was among its top recommendations. It also specifically mentioned the “hip healthy” certification featured on BabyBjorn’s product page. Increase Your Product Page Visibility in AI Search If you want AI to recommend your products, the best place to start is your product pages. Small improvements compound quickly. Clear descriptions. Structured data. Real reviews. Verifiable trust signals. Together, they shape how AI understands — and surfaces — your products. But product pages are just the start. First, download the Product Page AI Optimization Checklist. It tells you exactly what to review, update, and add to make your product pages AI-friendly. Then, learn how to build an AI ecommerce SEO strategy that improves your visibility across the entire buyer journey. AI visibility is possible for your products. Keep testing, keep tracking, and keep growing. The post How to Optimize Your Product Pages for AI Visibility appeared first on Backlinko. View the full article

Stocks are falling sharply on Wall Street Thursday, including a 1,000-point slump for the Dow Jones Industrial Average, as oil prices rise further because of the war with Iran. The S&P 500 sank 1.3% in afternoon trading, coming off a frenetic start to the week that saw financial markets swerve sharply, sometimes hour by hour. The Dow tumbled 1,046 points, or 2.1%, as of 2:04 p.m. Eastern time, and the Nasdaq composite was 1.1% lower. Financial markets are again following the cue of oil prices. They’re cranking up the pressure because of worries that a long-term spike could exhaust households’ ability to spend, grind down the global economy and push interest rates higher. A barrel of Brent crude, the international standard, rose 4.7% to $85.22 That’s up from close to $70 late last week. A barrel of benchmark U.S. crude climbed 8.1% to $80.67. U.S. crude last traded above $80 in August 2024. Oil prices rose after Iran launched a new wave of attacks against Israel, American bases and countries around the region. The war’s escalations are raising worries about how long disruptions will last for the production and transport of oil and natural gas in the region. Prices at U.S. gasoline pumps have already jumped because of them. The average price for a gallon is $3.25, up 9% from $2.98 a week ago, according to auto club AAA. To be sure, the U.S. stock market has a history of bouncing back relatively quickly following conflicts in the Middle East and elsewhere. That has many professional investors suggesting patience and riding through the market’s swings. “While further escalation remains a risk, we think the more likely outcome is an increase in market risk aversion that likely lasts only a short time until investors can see a winding down of hostilities,” according to Scott Wren, senior global market strategist at Wells Fargo Investment Institute. But if oil prices spike, like to $100 per barrel, and stay there, it could be too much for the global economy to withstand. Uncertainty about that has caused this week’s sharp swings, and much will depend on what happens with the Strait of Hormuz. Roughly a fifth of the world’s oil typically sails through the narrow waterway off Iran’s coast. Stocks of retailers fell to some of the U.S. market’s worst losses on Thursday. High gasoline prices mean their customers would have less to spend on other things. American Eagle Outfitters fell 14.8% even though it reported stronger profit and revenue for the latest quarter than analysts expected. Airlines also took sharp losses. Higher oil prices are increasing their already big fuel bills, while the war has left hundreds of thousands of passengers stranded across the Middle East. American Airlines lost 6.6%, United Airlines fell 6.8% and Delta Air Lines sank 5.3%. Stocks of smaller companies, meanwhile, took the heaviest losses. That’s typical when worries are growing about the strength of the economy and about interest rates rising. The Russell 2000 index of the smallest stocks fell 2.6%. Wall Street’s drop would have been worse if not for Broadcom. The chip company’s stock rose 3.6% after it reported stronger profit and revenue for the latest quarter than analysts expected. It’s one of Wall Street’s most influential stocks because it’s one of the biggest by total value, and CEO Hock Tan said it benefited from a 74% jump in revenue for AI chips. In the bond market, Treasury yields climbed as rising oil prices put more upward pressure on inflation, which could keep the Federal Reserve from cutting interest rates. The yield on the 10-year Treasury rose to 4.13% from 4.09% late Wednesday and from just 3.97% before the war with Iran started. The Fed could keep interest rates high to keep a lid on inflation. But high interest rates would also keep it more expensive for U.S. households and companies to borrow money, grinding down on the economy. The central bank had indicated it planned to resume its cuts to interest rates later this year, in hopes of giving a boost to the job market and economy. Because of the war and higher oil prices, traders have pushed their forecasts further into the summer for when the Fed could begin cutting rates again. Several reports on the U.S. economy also came in mixed. One said fewer U.S. workers filed for unemployment benefits last week than economists expected. That’s an encouraging signal for the job market. In stock markets abroad, indexes rebounded in Asia following historic losses a day before. South Korea’s Kospi jumped 9.6% to recover much of its 12.1% plunge from Wednesday, which was its worst drop ever. But indexes fell in Europe as oil prices began to accelerate. France’s CAC 40 fell 1.5%, and Germany’s DAX lost 1.6%. —Stan Choe, AP business writer AP Writers Kim Tong-hyung and Elaine Kurtenbach contributed. View the full article