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If you have your YouTube watch history disabled and you are now being prompted to turn it on if you want to receive recommendations, you're not alone. Watch history on YouTube is used to generate personalized recommendations on the platform—when it's disabled, suggested videos and channels are instead pulled from your likes, saves, and subscriptions rather than from videos you've watched. While some YouTube users want to be able to see a list of what they've viewed, many have watch history turned off for privacy reasons or to keep junk out of their algorithm in favor of a more curated experience. Some Reddit users have recently reported that their recommendations have disappeared from the YouTube homepage, replaced with a prompt to enable their YouTube watch history. The issue doesn't appear to affect everyone whose watch history is turned off—those who have had it disabled for many years seem to be more likely to encounter the prompt. As Mashable points out, this may be an effort to gain access to search histories for ad targeting. Manage your YouTube watch history You may not have to give in and give up more data to get your recommendations back, and the workaround may be as simple as turning your watch history on, refreshing the page, or doing a search, and turning it off again. To try this out, in the YouTube app, tap your profile photo and go to Settings > Manage all history > Controls and select Include the YouTube videos you watch. Refresh your homepage, then follow the same steps to unselect the setting. (Note that Turn Off will disable history, including searches, entirely.) On a TV or gaming console, you'll find this under Settings > Pause watch history; on a browser, go to My Activity > Controls. Even with watch history disabled, you can train your algorithm to produce better recommendations than whatever YouTube would otherwise suggest. The most basic tools are likes (and dislikes), subscriptions, and the bell, though you can also reject recommendations, create playlists, and even switch accounts to manage what you see. View the full article

Understand their goals and their weaknesses. By Jody Padar Radical Pricing – By The Radical CPA Go PRO for members-only access to more Jody Padar. View the full article

Understand their goals and their weaknesses. By Jody Padar Radical Pricing – By The Radical CPA Go PRO for members-only access to more Jody Padar. View the full article

Claude's plug-ins for third-party services, known as connectors, have previously covered work-focused apps such as Gmail and Slack. Now, it's adding a whole host of lifestyle apps to its Connectors catalog, including Spotify, Uber, Tripadvisor, Audible, Instacart, Intuit TurboTax, and AllTrails. There are more connectors on the way as well, Anthropic says, with the aim that you can do more inside the Claude chatbot interface. It's not dissimilar to the ChatGPT app store, which lets you add apps such as Photoshop and Apple Music. But do these plug-ins really offer the convenience that Claude claims? And is the experience any better than just using the apps themselves? Finding and adding connectors in Claude The connectors directory isn't new, but there are now many more lifestyle options in it: Head to the connectors page to browse through what's available, or click the + (plus) button on the prompt box in the Claude web interface and choose Connectors > Add connector from the pop-up menu. Each connector listing comes with a description of what the tool does and how it works, and once you've added a new connector you'll be returned to the main Claude interface. To use a connector, you just namecheck it in a prompt—there's no need to select it or even @ mention it, because Claude will figure out what you're referring to. There are now many more connectors to choose from. Credit: Lifehacker On the first run of a new connector, you'll be asked to give permission for the AI to connect to the app, using your login credentials. This works in the same way as most other plug-ins: You get a list of the permissions that Claude will have inside the app you're linking to, and you can then either confirm or reject the connection. To manage connectors and the permissions Claude has inside them, click the + (plus) button in the prompt box, then pick Connectors > Manage connectors. With Spotify, for example, there are separate permissions for accessing details of what you're currently playing, searching through the Spotify library, and creating playlists—you can enable or disable each of these permissions separately. Spotify was the first connector I tried, as it matches a similar one inside ChatGPT. I asked what the most popular Radiohead song was on Spotify, which it got wrong, and then requested a playlist of "hidden gems" and "lesser-known tracks" for R.E.M.—which surfaced such deep cuts as "Shiny Happy People" and "Man on the Moon" (two of the band's biggest hits). Credit: Lifehacker Not the best of starts, but some other playlist prompts—for chill-out jazz, for instrumental post-rock, for one-hit wonders of the '90s—worked better. I can imagine playing around with some of these playlist options when I don't really know the artists I'm interested in and aren't too concerned with specifics. You can't play the playlists inside Claude, though—you have to jump to Spotify to hear anything longer than a preview. And considering there are already so many ways to get AI-powered playlists (including inside Spotify itself), I'm not sure this Claude plug-in really adds all that much, even if AI can be trusted to curate music (which remains debatable). You can use Claude to help find Ubers, hotels, and hiking trailsI experimented with several other new connectors in Claude. When it comes to Uber, you're able to look up the current pricing for a ride, so you'll see an approximate ETA, how much the journey will cost you, and the travel options available. It's helpful, up to a point, but it's not all that much more convenient than just checking the app—and Claude always hands off the actual searching and booking to the dedicated Uber app. The Wyndham Hotels and Resorts connector was promising, not just bringing up results for hotels in a location, but also letting me compare pricing, user reviews, and features—a pool, a gym, free parking, and anything else you might be looking for. It's this kind of searching and summarizing AIs like Claude can be really good at. Claude asks for permission before connecting to apps. Credit: Lifehacker As far as I could tell by cross-referencing on the web, Claude didn't make any mistakes when weighing up the differences between my hotel picks, but I'm still not sure I'm ready to entirely trust my travel planning to AI just yet. AllTrails is another connector I took a look at, asking for a variety of weekend hiking options around my local area. I was easily able to look up walks based on time, user rating, and difficulty, and Claude helped me narrow down the different options I had and what each one involved. As with the other connectors here, I got some nicely formatted embedded previews within Claude itself. Again, though, it's not all that different to just using the dedicated AllTrails app from the start. Claude's AI adds the sheen of conversational interface, which makes searching and comparing a little more straightforward, but it's really just joining dots that are already there. The integrations are neatly done, but are only really previews. Credit: Lifehacker Having Claude sweep through your Gmail for meeting times and present the results in Slack is one thing (and something you could already do with the enterprise-focused connectors), but giving you limited access to Spotify's tools for building playlists is another. At the moment, these lifestyle extensions feel a little half-baked. I got that feeling with the Tripadvisor plug-in too, when I tried to look up the reviews of a local attraction inside Claude. The AI displayed a widget with details for the wrong location, told me that it had failed to find an accurate match from the Tripadvisor database, and advised me to check the Tripadvisor app directly, which I will be doing from now on. View the full article

And 10 questions to ask about yourself. By Martin Bissett Go PRO for members-only access to more Martin Bissett. View the full article

And 10 questions to ask about yourself. By Martin Bissett Go PRO for members-only access to more Martin Bissett. View the full article

Today, one of the biggest tech showdowns of the year begins. It’s the day on which the world’s richest man, Elon Musk, and the world’s most influential AI leader, Sam Altman, are expected to appear in court to issue their opening statements in the OpenAI trial. Here’s what you need to know about the high-stakes case. What is the OpenAI trial about? The trial centers around the very public dispute between Elon Musk and Sam Altman. Musk is suing Altman and OpenAI for allegedly deviating from their commitment to keep the company a nonprofit institution, as it was when Musk first invested millions of dollars in the then-upstart between 2015 and 2017. In 2018, Musk left the board of OpenAI. As Reuters reports, a year later, the company created a for-profit entity. Musk has alleged that this move went against the founding principles of the company that he invested in. The Tesla CEO says that instead, Altman and others sought to turn OpenAI into a “wealth machine” to enrich themselves. When does the trial start? The trial officially started yesterday, when jurors were selected. But today is the day many consider the trial to really begin in earnest. That’s when both Elon Musk and Sam Altman are expected to make opening statements in the case. The trial is being held in the U.S. District Court for the Northern District of California in Oakland. What is Elon Musk seeking? Musk primarily wants three things. First, he wants OpenAI to revert back into a nonprofit company. Second, he wants Altman and Greg Brockman, OpenAI’s CEO and president, respectively, removed from their positions. Third, Musk wants $150 billion in damages from both OpenAI and investor Microsoft, with this money going towards the charitable arms of OpenAI. How have Sam Altman and OpenAI responded? OpenAI and Altman have long publicly refuted Musk’s claims, alleging that Musk is motivated by jealousy. The company has claimed Musk had discussed converting OpenAI into a for-profit company and wanted to be its CEO, notes Reuters. Only after he failed to be appointed CEO, OpenAI has alleged, did Musk take issue with the company’s for-profit shift. In a post on Musk’s social media network, X, yesterday, OpenAI stated that it “can’t wait to make our case in court where both the truth and the law are on our side” and called the lawsuit “a baseless and jealous bid to derail a competitor.” What to watch for next? The biggest news out of the case today will be Elon Musk’s and Sam Altman’s opening statements. After that, both men are expected to take the stand for questioning during the trial. Additionally, other executives from both OpenAI and Microsoft are also expected to take the stand. When will a verdict in the case be given? That remains to be seen and largely depends on how long the jury takes to deliberate once all the evidence has been given. However, the U.S. District Judge presiding over the case, Yvonne Gonzalez Rogers, has stated that she wants jurors to begin deliberations by May 12. View the full article

Effective accounts receivable collection is essential for maintaining cash flow and financial stability. By implementing strategies like setting clear payment terms, prioritizing overdue accounts, and utilizing automation, you can improve your collection efforts. It’s additionally important to track key performance indicators to assess your effectiveness continually. Comprehending these practices can lead to significant improvements in your organization’s financial health. But what specific tactics can you incorporate to optimize your accounts receivable process? Key Takeaways Establish clear payment terms and promptly send invoices to minimize disputes and encourage timely payments. Systematically monitor overdue accounts and prioritize follow-ups based on the likelihood of payment. Implement ongoing training for staff to enhance their skills in collections and knowledge of policies. Utilize automation to streamline workflows, reduce manual tasks, and improve accuracy in the collections process. Send timely payment reminders to reduce Days Sales Outstanding and strengthen customer relationships through consistent communication. Definition of Accounts Receivable Collections Accounts receivable collections refer to the methodical approach businesses use to gather payments from customers for products or services provided. This systematic process involves sending invoices and statements, along with actively following up on past-due accounts to minimize outstanding debts. By employing an accounts receivable collections agency, you can streamline your collection efforts and guarantee timely cash flow. An accounts receivable management collection agency can help you develop a well-structured strategy that transforms credit sales into revenue. Prioritizing effective AR collections can improve your financial health, reduce bad debt, and support operational needs. Furthermore, consistent communication with clients leads to fewer payment disputes, ultimately maintaining healthier financial relationships and contributing to your business’s overall stability. Benefits of Good AR Collection Practices Effective accounts receivable collection practices offer numerous benefits that can greatly improve a business’s financial health. By ensuring consistent cash flow, you can meet operational expenses and invest in growth opportunities. Good collections minimize outstanding debts, reducing the bad debt ratio, which averages 1.5% of B2B receivables in the U.S. Streamlined AR processes accelerate money inflow, as 91% of CFOs report efficiency gains from digitization. Implementing clear payment terms boosts financial visibility, leading to faster payments and reduced Days Sales Outstanding (DSO). Additionally, a well-structured AR collection strategy not only improves operations but also nurtures better client relationships. Engaging an accounts receivable management company can provide effective ways to improve accounts receivable collections, further strengthening your financial stability. Prioritize Collection Efforts and Proactively Manage Invoicing When you prioritize collection efforts and proactively manage invoicing, you can considerably improve your cash flow and reduce the risk of bad debts. Start by identifying overdue accounts and focusing on those most likely to pay. Establish clear payment terms and swiftly send invoices to minimize disputes. Systematically monitor payment due dates Send proactive follow-ups before payments are overdue Optimize resource allocation in the direction of high-risk accounts Utilizing an accounts receivable process flowchart can help visualize your collection strategies. Consider investing in accounts receivable collection services to refine your approach. By maintaining positive customer relationships during the enforcement of payment timelines, you’ll create a collaborative environment that encourages timely payments, finally leading to accelerated collections and improved cash flow. Track Benchmarks and KPIs Monitoring key benchmarks and performance indicators is crucial for optimizing your accounts receivable processes and ensuring a healthy cash flow. By tracking metrics like Days Sales Outstanding (DSO), Average Days Delinquent (ADD), and the Collection Effectiveness Index (CEI), you can gain valuable insights into your AR debt collection efforts. Here’s a quick overview of these KPIs: KPI Target Purpose Days Sales Outstanding Below 30 days Measures efficiency of payment collection Average Days Delinquent Varies by industry Provides insight into overdue payment behavior Collection Effectiveness Index Close to 100% Evaluates effectiveness of collection efforts Regularly analyzing these KPIs helps you identify trends and areas for improvement, in the end enhancing your accounts receivable flowchart. Train AR Staff on Collections Best Practices Training your accounts receivable staff on collections best practices is essential for maintaining an efficient cash flow. By implementing ongoing training programs, familiarizing them with policies and procedures, and monitoring their performance, you’ll improve their skills in managing collections effectively. This structured approach not just reduces outstanding debts but additionally encourages better communication with clients, leading to quicker payments. Ongoing Training Programs Ongoing training programs are essential for accounts receivable (AR) staff, as they guarantee that everyone is well-versed in the latest collections policies and practices. Regular sessions keep your team compliant and efficient, enhancing their overall accounts receivable experience. Role-playing scenarios help staff navigate real-world collections challenges. Documented training materials track knowledge retention and identify improvement areas. Periodic refreshers secure consistency in approach and reinforce effective strategies. Incorporating feedback from your AR team into these programs allows you to address real challenges faced in collections. Policy and Procedure Familiarization Familiarity with accounts receivable (AR) collection policies and procedures is vital for ensuring that your team applies best practices consistently, which can greatly minimize payment delays and improve cash flow efficiency. Training your staff on the accounts receivable step by step process is critical. Regular sessions introduce new employees to these established guidelines, promoting accountability. Documenting training helps maintain a high knowledge standard, whereas periodic refreshers reinforce key practices. Emphasizing clear communication strategies equips your team to engage clients effectively, reducing disputes. Incorporating feedback mechanisms post-training identifies areas for improvement, enhancing your AR processes. In the end, aligning your training efforts with accounts receivable goals for performance review examples can lead to reduced Days Sales Outstanding (DSO) and greater financial stability. Performance Monitoring and Assessment Monitoring and evaluating performance in accounts receivable is crucial for optimizing collections and improving overall financial health. Training your AR staff on collections best practices guarantees they stay informed and effective. Regular training sessions should focus on: Updated collections policies and procedures to improve efficiency. Documentation of training for onboarding new employees with established guidelines. Customer communication strategies to nurture positive relationships and manage overdue accounts effectively. Leverage Accounts Receivable Automation Software Accounts receivable automation software serves as a strong tool for businesses looking to improve their collection processes. By implementing such software, you can reduce Days Sales Outstanding (DSO) by up to 30%, greatly enhancing cash flow. Automation streamlines invoicing, sending invoices and payment reminders automatically, which can boost efficiency by 91%, according to CFOs. It allows for real-time tracking of outstanding payments and generates insightful reports, making your accounts receivable debt collection more effective. Moreover, automating routine communications alleviates bottlenecks, enabling your AR staff to focus on complex issues. With these automated systems, customers can access self-service options, checking invoice statuses and making payments independently, leading to faster processing times and improved AR collection services. How Automation Can Help Collection Efforts When businesses implement automation in their accounts receivable processes, they considerably improve their collection efforts. Automation streamlines workflows, allowing you to focus on more complex issues during enhancing accuracy and efficiency. Timely payment reminders reduce Days Sales Outstanding (DSO). Automated responses handle inbound requests, freeing up AR staff. Insightful reports track collections and key performance indicators. Frequently Asked Questions How to Effectively Collect Accounts Receivable? To effectively collect accounts receivable, you should establish clear payment terms and guarantee your invoices are concise. Automate your invoicing process and set reminders to boost efficiency. Track key performance indicators, like Days Sales Outstanding, to measure your collection effectiveness. Openly communicate with customers about due payments, and consider offering early payment discounts to encourage timely payments. These strategies can help you improve cash flow and strengthen client relationships. What Are the 5 C’s of Accounts Receivable Management? The 5 C’s of accounts receivable management are essential for evaluating creditworthiness. First, Character evaluates a customer’s reliability based on their payment history. Next, Capacity examines their financial ability to meet obligations, often through financial statements. Capital highlights the financial resources available to the customer. Collateral refers to assets backing credit, offering security for lenders. Finally, Conditions consider external factors, like market trends, that may impact a customer’s ability to pay. Which of the Following Strategies Can Help Improve the Collection of Receivables? To improve the collection of receivables, you should implement automated invoicing and payment reminders. This can reduce Days Sales Outstanding markedly. Establish clear payment terms and communicate them effectively to minimize disputes. Use data analytics to identify high-risk accounts, allowing you to prioritize your collection efforts. Offering early payment incentives, like discounts, can encourage timely payments. Moreover, centralizing documentation guarantees your customers have easy access to invoices, speeding up the payment process. What Are the Three C’s of a Successful Collection Strategy? The three C’s of a successful collection strategy are clear communication, consistency in follow-ups, and a customer-centric approach. Clear communication means setting transparent payment terms from the start. Consistency in follow-ups involves sending timely reminders, which can boost payment rates considerably. A customer-centric approach focuses on building strong client relationships, nurturing trust. Conclusion Incorporating these seven crucial strategies can greatly improve your accounts receivable collection efforts. By establishing clear payment terms, prioritizing overdue accounts, and utilizing automation, you streamline your processes for better efficiency. Monitoring key performance indicators, such as Days Sales Outstanding, allows you to gauge effectiveness, whereas ongoing staff training improves communication skills. In the end, nurturing positive customer relationships cultivates trust and encourages timely payments, ensuring your organization maintains a healthy cash flow and financial stability. Image via Google Gemini and ArtSmart This article, "7 Essential Strategies for Effective Accounts Receivable Collection" was first published on Small Business Trends View the full article

Effective accounts receivable collection is essential for maintaining cash flow and financial stability. By implementing strategies like setting clear payment terms, prioritizing overdue accounts, and utilizing automation, you can improve your collection efforts. It’s additionally important to track key performance indicators to assess your effectiveness continually. Comprehending these practices can lead to significant improvements in your organization’s financial health. But what specific tactics can you incorporate to optimize your accounts receivable process? Key Takeaways Establish clear payment terms and promptly send invoices to minimize disputes and encourage timely payments. Systematically monitor overdue accounts and prioritize follow-ups based on the likelihood of payment. Implement ongoing training for staff to enhance their skills in collections and knowledge of policies. Utilize automation to streamline workflows, reduce manual tasks, and improve accuracy in the collections process. Send timely payment reminders to reduce Days Sales Outstanding and strengthen customer relationships through consistent communication. Definition of Accounts Receivable Collections Accounts receivable collections refer to the methodical approach businesses use to gather payments from customers for products or services provided. This systematic process involves sending invoices and statements, along with actively following up on past-due accounts to minimize outstanding debts. By employing an accounts receivable collections agency, you can streamline your collection efforts and guarantee timely cash flow. An accounts receivable management collection agency can help you develop a well-structured strategy that transforms credit sales into revenue. Prioritizing effective AR collections can improve your financial health, reduce bad debt, and support operational needs. Furthermore, consistent communication with clients leads to fewer payment disputes, ultimately maintaining healthier financial relationships and contributing to your business’s overall stability. Benefits of Good AR Collection Practices Effective accounts receivable collection practices offer numerous benefits that can greatly improve a business’s financial health. By ensuring consistent cash flow, you can meet operational expenses and invest in growth opportunities. Good collections minimize outstanding debts, reducing the bad debt ratio, which averages 1.5% of B2B receivables in the U.S. Streamlined AR processes accelerate money inflow, as 91% of CFOs report efficiency gains from digitization. Implementing clear payment terms boosts financial visibility, leading to faster payments and reduced Days Sales Outstanding (DSO). Additionally, a well-structured AR collection strategy not only improves operations but also nurtures better client relationships. Engaging an accounts receivable management company can provide effective ways to improve accounts receivable collections, further strengthening your financial stability. Prioritize Collection Efforts and Proactively Manage Invoicing When you prioritize collection efforts and proactively manage invoicing, you can considerably improve your cash flow and reduce the risk of bad debts. Start by identifying overdue accounts and focusing on those most likely to pay. Establish clear payment terms and swiftly send invoices to minimize disputes. Systematically monitor payment due dates Send proactive follow-ups before payments are overdue Optimize resource allocation in the direction of high-risk accounts Utilizing an accounts receivable process flowchart can help visualize your collection strategies. Consider investing in accounts receivable collection services to refine your approach. By maintaining positive customer relationships during the enforcement of payment timelines, you’ll create a collaborative environment that encourages timely payments, finally leading to accelerated collections and improved cash flow. Track Benchmarks and KPIs Monitoring key benchmarks and performance indicators is crucial for optimizing your accounts receivable processes and ensuring a healthy cash flow. By tracking metrics like Days Sales Outstanding (DSO), Average Days Delinquent (ADD), and the Collection Effectiveness Index (CEI), you can gain valuable insights into your AR debt collection efforts. Here’s a quick overview of these KPIs: KPI Target Purpose Days Sales Outstanding Below 30 days Measures efficiency of payment collection Average Days Delinquent Varies by industry Provides insight into overdue payment behavior Collection Effectiveness Index Close to 100% Evaluates effectiveness of collection efforts Regularly analyzing these KPIs helps you identify trends and areas for improvement, in the end enhancing your accounts receivable flowchart. Train AR Staff on Collections Best Practices Training your accounts receivable staff on collections best practices is essential for maintaining an efficient cash flow. By implementing ongoing training programs, familiarizing them with policies and procedures, and monitoring their performance, you’ll improve their skills in managing collections effectively. This structured approach not just reduces outstanding debts but additionally encourages better communication with clients, leading to quicker payments. Ongoing Training Programs Ongoing training programs are essential for accounts receivable (AR) staff, as they guarantee that everyone is well-versed in the latest collections policies and practices. Regular sessions keep your team compliant and efficient, enhancing their overall accounts receivable experience. Role-playing scenarios help staff navigate real-world collections challenges. Documented training materials track knowledge retention and identify improvement areas. Periodic refreshers secure consistency in approach and reinforce effective strategies. Incorporating feedback from your AR team into these programs allows you to address real challenges faced in collections. Policy and Procedure Familiarization Familiarity with accounts receivable (AR) collection policies and procedures is vital for ensuring that your team applies best practices consistently, which can greatly minimize payment delays and improve cash flow efficiency. Training your staff on the accounts receivable step by step process is critical. Regular sessions introduce new employees to these established guidelines, promoting accountability. Documenting training helps maintain a high knowledge standard, whereas periodic refreshers reinforce key practices. Emphasizing clear communication strategies equips your team to engage clients effectively, reducing disputes. Incorporating feedback mechanisms post-training identifies areas for improvement, enhancing your AR processes. In the end, aligning your training efforts with accounts receivable goals for performance review examples can lead to reduced Days Sales Outstanding (DSO) and greater financial stability. Performance Monitoring and Assessment Monitoring and evaluating performance in accounts receivable is crucial for optimizing collections and improving overall financial health. Training your AR staff on collections best practices guarantees they stay informed and effective. Regular training sessions should focus on: Updated collections policies and procedures to improve efficiency. Documentation of training for onboarding new employees with established guidelines. Customer communication strategies to nurture positive relationships and manage overdue accounts effectively. Leverage Accounts Receivable Automation Software Accounts receivable automation software serves as a strong tool for businesses looking to improve their collection processes. By implementing such software, you can reduce Days Sales Outstanding (DSO) by up to 30%, greatly enhancing cash flow. Automation streamlines invoicing, sending invoices and payment reminders automatically, which can boost efficiency by 91%, according to CFOs. It allows for real-time tracking of outstanding payments and generates insightful reports, making your accounts receivable debt collection more effective. Moreover, automating routine communications alleviates bottlenecks, enabling your AR staff to focus on complex issues. With these automated systems, customers can access self-service options, checking invoice statuses and making payments independently, leading to faster processing times and improved AR collection services. How Automation Can Help Collection Efforts When businesses implement automation in their accounts receivable processes, they considerably improve their collection efforts. Automation streamlines workflows, allowing you to focus on more complex issues during enhancing accuracy and efficiency. Timely payment reminders reduce Days Sales Outstanding (DSO). Automated responses handle inbound requests, freeing up AR staff. Insightful reports track collections and key performance indicators. Frequently Asked Questions How to Effectively Collect Accounts Receivable? To effectively collect accounts receivable, you should establish clear payment terms and guarantee your invoices are concise. Automate your invoicing process and set reminders to boost efficiency. Track key performance indicators, like Days Sales Outstanding, to measure your collection effectiveness. Openly communicate with customers about due payments, and consider offering early payment discounts to encourage timely payments. These strategies can help you improve cash flow and strengthen client relationships. What Are the 5 C’s of Accounts Receivable Management? The 5 C’s of accounts receivable management are essential for evaluating creditworthiness. First, Character evaluates a customer’s reliability based on their payment history. Next, Capacity examines their financial ability to meet obligations, often through financial statements. Capital highlights the financial resources available to the customer. Collateral refers to assets backing credit, offering security for lenders. Finally, Conditions consider external factors, like market trends, that may impact a customer’s ability to pay. Which of the Following Strategies Can Help Improve the Collection of Receivables? To improve the collection of receivables, you should implement automated invoicing and payment reminders. This can reduce Days Sales Outstanding markedly. Establish clear payment terms and communicate them effectively to minimize disputes. Use data analytics to identify high-risk accounts, allowing you to prioritize your collection efforts. Offering early payment incentives, like discounts, can encourage timely payments. Moreover, centralizing documentation guarantees your customers have easy access to invoices, speeding up the payment process. What Are the Three C’s of a Successful Collection Strategy? The three C’s of a successful collection strategy are clear communication, consistency in follow-ups, and a customer-centric approach. Clear communication means setting transparent payment terms from the start. Consistency in follow-ups involves sending timely reminders, which can boost payment rates considerably. A customer-centric approach focuses on building strong client relationships, nurturing trust. Conclusion Incorporating these seven crucial strategies can greatly improve your accounts receivable collection efforts. By establishing clear payment terms, prioritizing overdue accounts, and utilizing automation, you streamline your processes for better efficiency. Monitoring key performance indicators, such as Days Sales Outstanding, allows you to gauge effectiveness, whereas ongoing staff training improves communication skills. In the end, nurturing positive customer relationships cultivates trust and encourages timely payments, ensuring your organization maintains a healthy cash flow and financial stability. Image via Google Gemini and ArtSmart This article, "7 Essential Strategies for Effective Accounts Receivable Collection" was first published on Small Business Trends View the full article

Former executive told investigator examining company’s failure that audit firm had ‘most unsophisticated’ processView the full article

Audio recordings reveal Tom Harper told contractor to destroy material about an investigation into journalistsView the full article

In a landscape where traditional employment is waning, Shopify’s latest data reveals a burgeoning shift toward entrepreneurship that small business owners should take note of. As economic pressures continue to mount on conventional job markets, an increasing number of individuals are opting to start their own businesses, and the results are striking. Shopify’s analysis highlights three critical trendlines that illustrate this transformation: the rise in new business formations, the advantages of repeat entrepreneurship, and the increasing revenue generated by these businesses. This paints a compelling case for small business owners considering the leap into entrepreneurship or expanding their current operations. The first point worth noting is the significant surge in entrepreneurship. Since 2018, the number of Shopify merchants making their first sale has skyrocketed by sevenfold, illustrating a remarkable interest in self-employment. Coinciding with struggles in traditional employment—evidenced by 1.2 million job cuts in the U.S. in 2025, many attributed to advances in AI—this trend suggests that more individuals view entrepreneurship as a viable, if not preferable, path. As small business owners reassess their career trajectories, these numbers may prompt reflection. Compared to the shifting sands of a 9-to-5 job, starting a business could offer greater stability and growth potential, especially as the job market contracts. For those considering entrepreneurship, the data on repeat founders is particularly illuminating. Shopify reports that entrepreneurs who launch a second business tend to earn more than double the sales per shop compared to first-time founders. This aligns with existing academic research, indicating that prior business experience translates into improved performance in subsequent ventures. With knowledge in areas such as customer acquisition and operational management compounding over time, second-time founders enjoy significant advantages. Quote from Shopify: “The first venture is the hardest, but each one after benefits from accumulated knowledge.” This compounding of entrepreneurial skills could encourage small business owners to explore multiple ventures as they expand their business portfolios. Those who have previously navigated the ups and downs of business may find it easier to launch a new entity with the insights gained from their first experience. Another aspect to consider is revenue generation over time. Shopify’s findings reveal that merchants who began their journeys between 2017 and 2020 experienced a 25% growth in average sales from 2022 to 2025. This growth isn’t merely a function of improved personal or business skill sets; it reflects the broader expansion of the e-commerce market itself, which has grown from 14% to over 20% of total retail sales. With Shopify powering more than 14% of U.S. e-commerce, small business owners stand to benefit significantly from this trend as they position their operations in a growing digital marketplace. As small business owners evaluate these findings, it’s crucial to consider not just potential growth opportunities but also the challenges that come with them. The risk associated with starting a business remains a key concern. While the narrative has traditionally been that entrepreneurship is a high-risk endeavor, the data indicates that the landscape may be changing. With a growing number of tools designed to lower barriers, including platforms like Shopify and advancing AI technologies, the path to success has perhaps never been more accessible. However, small business owners should remain cautious. The increasing competition in the e-commerce space means that while the pie may be growing, slicing it successfully requires strategic positioning, smart marketing, and resilience. Relying solely on the boom in entrepreneurship without robust planning could lead to pitfalls, particularly for those sought-after repeat entrepreneurs who may face pressures to continually innovate and outpace the market. Overall, the landscape for small businesses is evolving rapidly. As more people turn to entrepreneurship and benefits stack upon previous experience, small business owners have the chance to carve out a fulfilling and potentially lucrative path. By keeping abreast of these trends, embracing technology, and remaining adaptable, they have the opportunity to thrive in a transforming economy. For more about Shopify’s insights on entrepreneurship, check the original report here. Image via Google Gemini This article, "Entrepreneurship Surges as Traditional Jobs Decline, Data Shows" was first published on Small Business Trends View the full article

In a landscape where traditional employment is waning, Shopify’s latest data reveals a burgeoning shift toward entrepreneurship that small business owners should take note of. As economic pressures continue to mount on conventional job markets, an increasing number of individuals are opting to start their own businesses, and the results are striking. Shopify’s analysis highlights three critical trendlines that illustrate this transformation: the rise in new business formations, the advantages of repeat entrepreneurship, and the increasing revenue generated by these businesses. This paints a compelling case for small business owners considering the leap into entrepreneurship or expanding their current operations. The first point worth noting is the significant surge in entrepreneurship. Since 2018, the number of Shopify merchants making their first sale has skyrocketed by sevenfold, illustrating a remarkable interest in self-employment. Coinciding with struggles in traditional employment—evidenced by 1.2 million job cuts in the U.S. in 2025, many attributed to advances in AI—this trend suggests that more individuals view entrepreneurship as a viable, if not preferable, path. As small business owners reassess their career trajectories, these numbers may prompt reflection. Compared to the shifting sands of a 9-to-5 job, starting a business could offer greater stability and growth potential, especially as the job market contracts. For those considering entrepreneurship, the data on repeat founders is particularly illuminating. Shopify reports that entrepreneurs who launch a second business tend to earn more than double the sales per shop compared to first-time founders. This aligns with existing academic research, indicating that prior business experience translates into improved performance in subsequent ventures. With knowledge in areas such as customer acquisition and operational management compounding over time, second-time founders enjoy significant advantages. Quote from Shopify: “The first venture is the hardest, but each one after benefits from accumulated knowledge.” This compounding of entrepreneurial skills could encourage small business owners to explore multiple ventures as they expand their business portfolios. Those who have previously navigated the ups and downs of business may find it easier to launch a new entity with the insights gained from their first experience. Another aspect to consider is revenue generation over time. Shopify’s findings reveal that merchants who began their journeys between 2017 and 2020 experienced a 25% growth in average sales from 2022 to 2025. This growth isn’t merely a function of improved personal or business skill sets; it reflects the broader expansion of the e-commerce market itself, which has grown from 14% to over 20% of total retail sales. With Shopify powering more than 14% of U.S. e-commerce, small business owners stand to benefit significantly from this trend as they position their operations in a growing digital marketplace. As small business owners evaluate these findings, it’s crucial to consider not just potential growth opportunities but also the challenges that come with them. The risk associated with starting a business remains a key concern. While the narrative has traditionally been that entrepreneurship is a high-risk endeavor, the data indicates that the landscape may be changing. With a growing number of tools designed to lower barriers, including platforms like Shopify and advancing AI technologies, the path to success has perhaps never been more accessible. However, small business owners should remain cautious. The increasing competition in the e-commerce space means that while the pie may be growing, slicing it successfully requires strategic positioning, smart marketing, and resilience. Relying solely on the boom in entrepreneurship without robust planning could lead to pitfalls, particularly for those sought-after repeat entrepreneurs who may face pressures to continually innovate and outpace the market. Overall, the landscape for small businesses is evolving rapidly. As more people turn to entrepreneurship and benefits stack upon previous experience, small business owners have the chance to carve out a fulfilling and potentially lucrative path. By keeping abreast of these trends, embracing technology, and remaining adaptable, they have the opportunity to thrive in a transforming economy. For more about Shopify’s insights on entrepreneurship, check the original report here. Image via Google Gemini This article, "Entrepreneurship Surges as Traditional Jobs Decline, Data Shows" was first published on Small Business Trends 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 Anker Soundcore Nebula P1 portable projector has dropped to $639 from its usual $799, and price-trackers show this is the lowest it has reached so far. Here, the hinge-based body does most of the heavy lifting—instead of stacking books or adjusting furniture to get the angle right, you tilt the projector head itself until the image lines up with your wall or screen. It sounds simple, but in practice, it makes setup faster and less frustrating, especially in smaller rooms. Anker Soundcore Nebula P1 Portable GTV Projector with Detachable Speakers $639.00 at Amazon $799.00 Save $160.00 Get Deal Get Deal $639.00 at Amazon $799.00 Save $160.00 The detachable speakers add to that flexible setup. Each one pushes 10W and can be placed closer to where you are sitting, which creates a wider soundstage than you would expect from a compact projector. Around the back, the port selection keeps things simple with HDMI 2.1, USB-A, AUX, and USB-C for power, which is enough for a console, laptop, or streaming stick. The software side runs on Google TV, and the included remote has a built-in microphone along with dedicated buttons for YouTube, Netflix, and Prime Video, so jumping between apps feels quick. The bigger limitation is portability. There is no internal battery, so using it outdoors or in a different room means carrying a power source, which takes away some of the convenience the design suggests. As for the picture quality, the 650 ANSI lumen brightness of this projector works best in a dark room, where colors look clean and bright scenes have a decent punch. Turn on the lights, though, and the image starts to lose impact quickly. Also, while the Soundcore Nebula P1 outputs at 1080p using pixel-shifting and looks sharp for most content, fine text and small UI elements can appear slightly rough around the edges. Setup is mostly hands-off, with auto keystone and focus running at startup, but features like screen fitting and obstacle avoidance still depend on the Nebula app instead of happening directly on the device—it gets the job done, though it is not as seamless as fully automatic systems. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods 4 Active Noise Cancelling Wireless Earbuds — $148.99 (List Price $179.00) 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) Apple iPad 11" 128GB A16 WiFi Tablet (Blue, 2025) — $319.97 (List Price $349.00) Fire TV Stick 4K Plus Streaming Player With Remote (2025 Model) — $29.99 (List Price $49.99) Fitbit Versa 4 Fitness Smartwatch (Black) — $149.95 (List Price $199.95) Deals are selected by our commerce team View the full article

AI has made it easy to generate software code, but some open source projects have stopped taking code submissions from the public, citing a deluge of low quality code or code that doesn’t match project needs. Warp, maker of tools for AI coding, is moving in the opposite direction. It’s making its desktop agentic development environment (ADE) software open source and even encouraging users to contribute new features with the help of AI. The ADE lets humans and AI agents work together to write code. Founder and CEO Zach Lloyd says software developers typically have their own preferences on tools and working styles, and he anticipates the program will let some of its nearly 1 million users add new features that best suit their needs faster than the Warp team could do on its own. “One thing that I’ve realized the longer I’ve been building for developers is that they have very diverse workflows,” Lloyd says. “So the way that we can get Warp to be the most powerful product for the greatest number of developers is to let those developers build the pieces that our internal team probably doesn’t have the bandwidth to do.” Still, Lloyd doesn’t anticipate simply accepting bundles of code from strangers on the internet. Instead, developers interested in adding a new feature or addressing an issue in the code can propose a plan on Warp’s GitHub issues page. Warp’s AI agents will then take a first stab at responding, including asking for more details on what a developer is looking to do and potentially generating a fleshed-out specification. A human at Warp will ultimately make the call on whether it’s a change the company would want to see in the app. “The idea is that agents do a bunch of the grunt work around triage and spec’cing out initial ideas, but humans are kind of still in the loop deciding what to build, and providing instruction on how to do it,” says Safia Abdalla, a software engineer at Warp. Once an idea is approved, developers will be able to get to work and, if they wish, they can use Warp’s own Oz orchestration software to manage agents building the code in the cloud. At least at the start, Warp will pay for Oz usage and AI credits to do so, Lloyd says. If developers prefer, they can also work on their own computers with tools of their choice and submit a pull request through GitHub when the work is done and ready for review by AI agents and humans at Warp. “We will ultimately code-review everything and make sure that the stuff that gets merged is high quality,” he says. Not all of Warp’s software will be open source: The cloud-based elements that make up much of Warp’s enterprise business will still be proprietary, at least for the time being, and there may be elements of the desktop app designed for specific clients or used to test not-yet-available AI models that won’t be released to the public, Lloyd says. And with the source available, users will be able to make their own changes to the software for their own use without submitting it back to Warp. “I think we’re in an age where so many people can build software, they can take forks, they can make it work for them how they want it to work,” Lloyd says. “If they want to do that, that’s totally fine.” Warp has already evolved rapidly, starting from an AI-empowered version of the command line terminals programmers use for precision control of their computers and evolving to add a coding environment designed for collaboration with AI agents. That, in turn, was followed by Oz, its cloud-based AI orchestration software. In theory, with the desktop software open source, someone could create their own version of the development environment and attempt to compete with Warp. But Lloyd says he’s not too concerned about the risk, especially since the software license will prevent anyone from distributing a closed-source version. And in addition to helping the product better match the needs of its users, going open source will also let Warp offer a public demonstration of what’s possible with its AI tools. “It’ll help our enterprise business by being a sort of showcase app for what an agent-powered software project can be like,” Lloyd says. View the full article

Every brand holds its claims, and somewhere in the archive of its digital life, there’s proof to back them up. The AI assistive engine (the systems behind ChatGPT, Perplexity, and Google AI Overviews) holds that proof too, scattered across its training data and retrieval index, alongside competitors’ claims. The audience has a need but no vocabulary to bridge the gap between what they want and what the brand or the engine already knows. All three lack the same thing: a frame, the interpretive context that turns scattered information into a narrative worth transmitting (for the brand), citing (for AI), and acting on (for the user). This is where the claim-frame-prove (CFP) process comes in. Claim and proof are mechanical, and frame is strategic. Claim and prove are mechanical work the engine can verify. Frame is the strategic move only the brand can make. Why AI can’t make the leap your brand needs CFP runs claim by claim, fact by fact. The brand’s full picture is built from many CFP cycles compounding: each claim framed and proven becomes a fact in the corpus, and the cumulative weight of those facts is what positions the brand to dominate. AI can join known facts, but it can’t leap to a new one that benefits your brand. Given Facts A and B, AI can derive Conclusion C that follows logically. That’s standard inference, and the engines do it well. What they can’t do reliably is the move a creative human makes constantly: looking at A and B, reaching to a non-obvious J that’s commercially beneficial, and then constructing the logical bridge so the engine can use it. From here, I’ll use the alphabet as the scale: C is the obvious commercial reach, J is the non-obvious, and Q is the most ambitious leap a brand can sensibly make. Two examples from my own positioning make the difference concrete. A + B → C A: I coined answer engine optimization in 2017. B: I run a brand engineering company. The obvious conclusion (C) the engine produces unprompted: my work is connected to AEO implementation. True, useful, but close to the facts, and the engines will get that right without my help. A + B → J From the same A and B, reaching further. J: I’m the practitioner most likely to have learned what works and what doesn’t from a decade of operational data nobody else has. Same starting facts, completely different commercial outcome, and the engine doesn’t make this leap on its own. It needs me to build the bridge for it. That second move — from A and B to J — is strategic claim bridging: selecting which non-obvious J benefits the brand from the space of derivable conclusions, and then constructing the logical connection from accepted facts to that chosen J so the engine transmits it as fact rather than as the brand’s opinion of itself. Two operations packed into one move: the strategic part is choosing J, and the bridging part is making the inference watertight. AI won’t choose what’s best for your brand AI doesn’t choose the J that’s good for your brand. You do. That choice, and the bridge that proves it, is the work AI has no commercial stake in, and a future (more capable) AI without your stake just produces a more sophisticated version of the same problem. Whether AI can be creative is contested ground. The narrower claim holds regardless: even when AI produces a novel-looking output, it has no commercial intent guiding which J to derive. From the same A and B, an AI could just as easily produce a damaging J as a beneficial J. It has no skin in your commercial game. A creative marketer does both things at once: reaches imaginatively to a non-obvious J, and chooses the J that serves the brand. That’s the move AI engines can’t reach, and it’s why the frame has to come from someone placing the information online (the brand, a client, or an independent source). The disposition that lets you see this work is what I’ve been calling “empathy for the machine,” a phrase I started using in client consulting around 2011-2012 (originally as “empathy for the beast,” retired once I got more serious about the business side of digital marketing), and first published formally in 2019. It’s the discipline of stepping outside your own perspective to see what the machine actually struggles with. That advice applies to anything in SEO/AAO — in this case, specifically to when it grounds, attributes, and synthesizes claims about your brand. Unfortunately, brands all too often produce material aimed at human readers and assume the machine will figure out the rest. With a little empathy for the machine, brands design material the machine can use as its own interpretation (feed the beast). This produces three different levels of brand-AI communication, each one building on the previous. Levels 1 and 2 are the foundations every brand needs in place, and Level 3 is where framing enters, and what this article is designed to change your thinking. Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with Level 1: Scattered proof of claims Proof exists, but there’s nothing linking it to the claim. This is where most brands sit, and it leaves the engine to perform inference over whatever it can find. The brand publishes Claim A on its website. Proof Z exists somewhere else: a conference program, an industry database, a Wikipedia citation, and a trade publication from four years ago. The brand assumes the engine will connect the two. To connect them, the engine has to perform inference. Can it derive the conclusion that this brand is credible for this claim, given scattered premises across different domains, formats, and varying source authority? There’s no copy stating the connection, no hyperlinks pointing from claim to proof, and no schema encoding the relationship. That depends almost entirely on how confidently the machine already understands the entity, and that runs on three sub-levels. If the machine has no confident understanding of the brand, and the proof isn’t explicitly linked, no connection happens. The proof might as well not exist. If the machine has no confident understanding of the brand, but the proof is explicitly linked, the connection happens because the link does the work that the entity resolution couldn’t. If the machine has a strong, confident understanding of the brand, the connection happens even without the link, because a well-resolved entity shortens the logical distance the machine has to traverse (linkless links, as I’ve called them). The link still adds confidence (more than one path always does), but it’s no longer load-bearing as the entity carries the work. The implication runs through the rest of the pipeline. Entity clarity in the knowledge graph isn’t a nice-to-have sitting alongside content work. It’s the variable that decides whether your content work has to carry all the weight or almost none of it. Any proof that isn’t explicitly linked is missed at sub-level one, caught at sub-level two, and confidently embedded at sub-level three. When entity understanding is weak, the result is familiar to anyone tracking AI visibility: a meritorious brand appears occasionally, and when it does, the wording is hedged, and the brand sits mid-to-low-pack. The engine did the best inference it could, and, being a responsible probability engine, it hedged. Worse, opportunities for inclusion are throttled across adjacent queries the fact should have pulled the brand into, because the fact was never connected to the proof that would have warranted the inclusion in the first place. What happens when Level 1, scattered proof of claims, is done well? Brand X is infrequently mentioned, unconvincingly, as a provider of Y. Level 2: Connected proof of claims Here, the brand explicitly connects claim to proof through a combination of copy, hyperlinks, and schema. It also closes the inference gap by providing what the engine would otherwise have to figure out. The brand publishes Claim A and explicitly connects it to Proof Z, with the logical thread stated in copy, anchored by hyperlinks to the proof, and encoded in schema: a fact with a significant number of supporting pieces of evidence joined to it three ways, leaving nothing for the engine to infer. Connected proof of claims is a spectrum, not a switch. At the low end, you’ve connected some of your proof, which already beats Level 1 because the engine no longer has to figure out the connections you’ve made, but it’s still figuring out the ones you haven’t. If your competition has connected more of theirs, you’re still losing the comparison on the proof you left scattered. At the high end, you’ve connected all of it: every claim joined to every piece of supporting evidence, nothing scattered, and nothing left for the engine to guess at. Most brands sit somewhere between scattered and connected simply because they’ve connected only the most obvious proof, and the AI may well have already figured the obvious ones out for itself: the links don’t teach it anything it didn’t already know. With connected proof of claims done comprehensively for a given claim, the engine has enough corroboration to back the brand confidently, and the claim becomes fact in the corpus. Confidence transfers cleanly because there’s nothing to guess at. Connected proof of claims is also a great weapon for a smaller brand competing with a bigger one: a specialist accounting firm with 50 pieces of proof, all explicitly connected to a specific positioning, beats a Big 4 with thousands of unconnected pieces on that specific positioning, because connection is what turns proof into substance that the engine can transmit. What happens when Level 2, connected proof of claims, is done well? Brand X is frequently mentioned convincingly as a provider of Y. Get the newsletter search marketers rely on. See terms. Level 3: Framed proof of claims This is where framing enters, and where strategic claim bridging earns its name. For each claim that matters, the brand publishes Claim A, connects the proof, and then does the thing the engine can’t do (and the audience is unlikely to do either, for that matter). It reaches the non-obvious J that benefits the brand, and constructs the bridge from A and B to J in language the engine can transmit. Not merely “we are the leader in X, demonstrated by Y,” but the frame: Why Y matters for the specific problem this audience faces. What Z signals about trust in this particular market. How W translates to the outcome the prospect actually cares about at the moment of decision. A frame is a logical inference from corroborated facts, where the brand chose where the inference would land. For example: “Jason Barnard coined answer engine optimization in 2017, made dated public predictions about how the field would unfold, and those predictions came true, his predictions about where the field is going next are credible.” Every component is verifiable independently, and every connection between components is logical. The J the bridge reaches to is the one I chose, not the J the engine would have generated unprompted. One well-constructed frame makes one claim into fact in the AI’s voice. Run that across the claims that matter, and the cumulative weight is what shifts a brand from “frequently mentioned convincingly” to “almost always mentioned as the leading provider”: dominance is a stack of well-framed facts, not a single masterstroke. The result: the AI doesn’t merely confirm, it enthuses. “Brand X leads in Y, and here is why that matters for your situation.” The engine transmits the frame wholesale, in the language you chose, to the audience you specified, with a reason to keep coming back. The machine didn’t generate the narrative; it relayed it warmly. What happens when Level 3, framed proof of claims, is done well across the claims that matter? Brand X is almost always mentioned as the leading provider of Y, and dominates the space. Each level builds on the previous: connected proof of claims requires scattered proof of claims connected, and framed proof of claims requires connected proof of claims bridged strategically. Most brands are only halfway to framed proof of claims The brands that think they’re at framed proof of claims are usually at framed proof of claims for humans, and scattered proof of claims for machines. Marketing and narrative work supplies frames to humans all the time, and plenty of brands do it well. What almost no brand does is supply frames the machine can use, and the gap between the two is where framed proof of claims is most powerful. Some brands operate below even that and are effectively standing still: published facts at the surface, few proof connections, and no interpretive content the machine can use for any purpose. The signature objection from a standing still brand is the same in every consulting room: “We already do this, our website explains who we are.” The website does that. The website is doing zero work to help the machine with framing. The cost of standing still isn’t visible until a model update or two down the line. Brands that think they’re at framed proof of claims are usually investing harder in the wrong layer (content), while the layer that matters (framing and, ideally, joining the dots) compounds for someone else. The gap widens every year. If you have content that doesn’t frame effectively or join the dots with links to proof, you’re leaking huge value, and pushing through connection and framing is the best return on past investment you can make right now: you’re doing the heavy lifting for the machines, and they’ll reward you for giving them this extremely valuable context on a plate. Three structural conditions separate framed proof of claims from marketing-and-narrative-as-usual, and missing any one collapses the brand back to connected proof of claims or lower. The entity has to be well-established, well-resolved, and trusted, because a frame can’t anchor to a vague brand. The underlying proof has to be connected, because most brands have fluent marketing prose on top of scattered proof, which is scattered proof of claims with prettier wallpaper. The bridge itself has to be strictly logical, because machines read logic first and tone second, and a logically broken bridge fails, however well it’s written. The better AI gets, the more framing matters Smarter AI rewards better framing rather than replacing it, and the reason is the same selection pressure SEO practitioners have been operating under since the early 2000s. There’s a seductive and entirely wrong conclusion to draw from rapid improvement in AI reasoning: that engines will eventually figure out how to frame brands correctly without help. The opposite is true. The engine rewards the brand whose assets reduce its own workload for the same or better result. Search engines reward sites that are easy to crawl, render, and classify. Knowledge Graphs reward entities that are easy to resolve. AI assistive engines reward content that is easy to ground, verify, and transmit confidently. Where the engine has to choose between two roughly equivalent candidates, the candidate that demands less computation, less inference, and less guesswork wins. Framed proof of claims is that principle operating at the bridging layer. A more capable engine encountering this level has the bridge handed to it ready-made. It doesn’t have to figure out the frame, it transmits the bridge the brand supplied, fluently and confidently, with the engine’s full reasoning capability now amplifying rather than substituting for the framing work. A more capable engine without a frame falls back to inference over scattered evidence, which is expensive, ambiguous, and produces hedged output. Every improvement in reasoning capability makes the hedging more detailed and the noncommittal language more sophisticated, but the underlying problem isn’t capability, it’s the absence of a frame to amplify. The engine is doing more work for a worse result, and that’s the exact failure mode the engine’s selection pressure is designed to penalize. The gap between those two outcomes is the framing gap, and it widens with every generation. Brands implementing only connected proof of claims don’t lose ground in absolute terms, they lose ground relative to brands implementing Framed Proof of claims faster every year, because the engine increasingly rewards assets that let it deploy its growing capability productively rather than waste it on guessing and hedging. The selection pressure that rewarded fast websites in 1998, clean HTML in 2003, and structured data in 2015 rewards framed proof of claims now. The mechanism of gaining a competitive advantage by reducing costs for the AI for the same or better results hasn’t changed — and probably never will. The framed proof of claims trajectory rises steeply and continues climbing. The connected proof of claims trajectory rises gently and flattens. The shaded area between the two lines is labeled the framing gap and visibly widens with each generation. Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with The bridge stays human The bridge is human territory, and it stays human because it requires commercial intent specific to the brand that the engine doesn’t have. Everything the machine does well will get better: retrieval, connection, pattern extraction, and synthesis. None of that helps the brand whose evidence the machine can see but can’t bridge meaningfully to a beneficial conclusion. Whether AI confirms your brand, overlooks it, or champions it comes down to one discipline: strategic claim bridging, claim by claim, fact by fact. It’s the last layer of brand-AI communication that won’t yield to automation, if it yields at all. This is the 11th piece in my AI authority series. The first, “Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it,” introduced cascading confidence. The second, “AAO: Why assistive agent optimization is the next evolution of SEO,” named the discipline. The third, “The AI engine pipeline: 10 gates that decide whether you win the recommendation,” mapped the full pipeline. The fourth, “The five infrastructure gates behind crawl, render, and index,” walked through the infrastructure phase. The fifth, “5 competitive gates hidden inside ‘rank and display’,” covered the competitive phase. The sixth, “The entity home: The page that shapes how search, AI, and users see your brand,” mapped the raw material. The seventh, “The push layer returns: Why ‘publish and wait’ is half a strategy,” extended the entry model. The eighth, “How AI decides what your content means and why it gets you wrong,” covered annotation — the last gate where you’re alone with the machine. The ninth, “Why topical authority isn’t enough for AI search,” opened the competitive phase proper with topical ownership. The tenth, “The funnel flip: Why AI forces a bottom-up acquisition strategy,” named the process. Up next: The method to find where your content fails in the AI engine pipeline, and why the window to fix it is closing. View the full article

Earlier this month was the annual Ask a Manager salary survey. This week, let’s compare paid time off. Fill out the form below to anonymously share how much paid time off you get, in the context of your field and other relevant factors. (Do not leave your info in the comments section! If you can’t see the survey questions, try this link instead.) When you’re done, you can view all the responses in a sortable spreadsheet. (Note: I have been unable to figure out how to make this work for jobs like teachers who get summers off but will happily take suggestions on that for next time.) Loading… The post how much paid time off do you get? appeared first on Ask a Manager. View the full article

Beijing signals relaxation of export ban that was imposed at the start of Iran conflictView the full article

Wil Reynolds, founder and CEO of Seer Interactive, is challenging SEOs to rethink what success looks like in a world increasingly shaped by AI. In his SEO Week session, “SEO is a performance channel, GEO isn’t. How do you pivot?”, Reynolds said many marketers are focused on the wrong outcomes — and producing work that people don’t believe. Marketing isn’t just about being seen Reynolds opened by pushing back on the idea that visibility alone is the goal of marketing. “Marketing was never just to be seen or be visible,” he said. “You had to turn that visibility into something — believing something about your brand… And then they ultimately have to choose you.” He described a progression that marketers need to focus on: being seen, being believed and being chosen. “It’s how you take your time with people, and turn them from seeing you, into believing something about you,” he said. “I got the ranking, job finished,” he added. “Job’s not finished.” Reynolds also questioned the value of surface-level success metrics. “I got a lot more followers, but they don’t pay you,” he said. Low-quality marketing is everywhere Reynolds pointed to common marketing tactics — including automated outreach — as examples of work that doesn’t create value. “That’s not marketing,” he said, referring to spam-like SMS messages. Those tactics made him reflect on his own past work, he said. “I started looking at the stuff that I used to do… was that really marketing?” he said. “Some of us are strategists. Some of us are loopholists,” he said. “You’ve got to make a decision today.” The industry is producing ‘zombie content’ Reynolds criticized the widespread use of scaled, templated content designed primarily to rank. He used broad listicle-style pages as an example. “Why would you write content saying best restaurants in Minnesota when nobody that’s a human looks for the best restaurant in Minnesota?” he said. He described this type of content as “zombie content.” “That’s what we do,” he said, describing how marketers repeat what already ranks instead of doing something different. He also described how many marketers approach content creation. “I’m going to look at the top 10 and look at what they did slightly wrong… and I’m only going to do it slightly better,” he said. Short-term tactics vs. long-term brand building Reynolds contrasted short-term SEO tactics with long-term brand building. “Some people like to win in decades,” he said. “Other people like to win quarter to quarter.” He described how many teams focus on immediate results. “What works this quarter to get my boss off my back long enough so I can survive the next quarter?” he said. That approach leads to work that people don’t actually want, he said. “You will never produce a thing that anyone wants if you continue to play that,” he said. SEO success doesn’t translate to AI visibility Reynolds shared an example involving “ethical jeans” to show how SEO and AI results can differ. One brand ranked well in Google without being known for ethical practices, while another brand that invested in ethical production ranked much lower. In AI-generated answers, that outcome changed. “If that worked, if it was the same, that brand would be showing up in AI models,” he said. “And they showed up in none.” He connected this to credibility. “Nobody believed them,” he said. “Nobody chose them.” Visibility without belief doesn’t lead to outcomes Visibility alone isn’t enough, Reynolds said. “If you have all the visibility in the world and people don’t believe you or trust you, then you’re not going to get chosen,” he said. Visibility is only part of the process, he said. “This visibility is just an opportunity,” he said. “That’s all it is. … Iit is not the job to be done.” What people say matters Reynolds suggested looking at platforms like Reddit to understand how people actually talk about brands. “Go to Reddit… look at all the brands,” he said. “You find out that humans don’t believe you. And they have to pay you for you to stay in business. He contrasted that with how brands present themselves in content. “Not only did they not think you’re number one — they don’t think you’re number 100,” he said. The wrong metrics are being measured Marketers often focus on metrics that are easy to track rather than meaningful, Reynolds said. “We’re measuring the easy stuff to measure,” he said. “The real work is in the hard-to-measure stuff.” He encouraged comparing visibility metrics with signals tied to outcomes. “If your visibility is skyrocketing and your pipeline is flat, that’s bad,” he said. Watching real users changes the picture Reynolds described research his team conducted by observing real people using AI tools. “When you actually watch people do the job… your eyes open so much wider,” he said. One person typed four words, while another typed more than 100 words for the same task, he said. He also noted that AI tools often suggest additional steps or actions beyond what users ask for, and people frequently accept those suggestions, he said. Start with your brand Marketers should focus on how their brand appears in AI-generated answers, especially for branded queries, Reynolds said. “You spend all this money trying to get people to know your brand… and then you don’t want to make sure that answer’s right?” he said. AI can shape your brand narrative Reynolds shared an example where AI-generated responses surfaced incorrect information about his company. “So now it’s showing up everywhere,” he said. He described responding by publishing content to address the claim directly. “If it’s false, then I’ve got to fight that,” he said. There is too much content “There’s too much content out there,” he said. He described shifting his approach. “I’m trying to become a curator,” he said. Rethinking performance Reynolds shared examples of how different traffic sources perform. “My direct converts 1.5 times better than my SEO,” he said. “My social, five times better.” A final question for marketers Reynolds ended by asking marketers to rethink their priorities: “Are you willing to sacrifice a little bit of this visibility game to be more believable?” View the full article

Windows users contend with a lot of updates. There's a new update every month on the stable channel, and every week on the Windows Insider channel. But not all updates are created equal. Some are mission-critical, with important security patches you won't want to miss. On the other hand, some can create issues themselves, introducing bugs or new features you don't want. Until now, there wasn't much you could do when an update showed up. You could try to delay, but you'd be forced to install it a week later, sometimes in the middle of important work. With the latest Insider update, however, Microsoft is trying to fix that. Now, the company is testing a roundabout way to delay updates forever (though from a security standpoint, you shouldn't), as well as a process for installing updates that won't disrupt your workflow. How to delay Windows updates foreverIn the Windows Insider update rolling out this week, you can pause updates for up to 35 days at a time. That doesn't mean you have to update your PC once those 35 days are up, however. You can keep doing this manually indefinitely. There are no limits. When you have the option on your end, go to Settings > Windows Update > Pause Updates. You'll see a new date picker here to extend the update. Here, you can choose a date you want Windows to install that update—perhaps after the deadline for an important project, so you can be sure that the update won't interrupt your work. You'll need to enroll your PC in the Windows Insider program if you want to try this new feature out, however. Microsoft has not officially rolled it out in a public Windows update, so unless you want to join Microsoft's beta program, you'll need to wait and see if the company decides to release this feature in the near future. Credit: Microsoft Why you shouldn't delay updates foreverThere are some caveats here. First, you'll have to do this manually each time to extend the pause period. Second, there's no option to cherry-pick which updates get delayed. It's just one option to pause updates, which can include multiple pending updates on your PC, even for drivers or security updates. When you pause updates, you lose out on all of it. The monthly Windows update isn't just about new features you may or may not want: It also includes critical security updates that patch vulnerabilities and help protect your computer from attacks. In addition, it fixes longstanding bugs and issues, and introduces updates at the firmware and driver level that help improve the performance of your GPUs, memory, and peripherals. You can use this new "Pause Updates" feature to decide when exactly to install a monthly update (perhaps after waiting for a week or two), but from a security standpoint, it's not a good idea to delay updates indefinitely, just because you can. Other changes to updates on Windows 11You'll also be able to skip new updates when you're first setting up your Windows PC. During setup, you'll see a new Update Later button, which should get you to your desktop faster. When you do eventually install the update, the experience should be better than before. To reduce update fatigue, Microsoft is now trying to coordinate security, driver, and feature updates so they all appear together once a month. You'll also get a detailed view of all available updates in the Windows Update section. In addition, "Shutdown" and "Restart" will soon be available at all times—even when there is a pending update. You won't be forced into the "Update and restart" cycle just because you've delayed updates before. Credit: Microsoft View the full article

While it sounds silly, especially since I have a variety of construction skills, I lay awake some nights stressing about our stairs. We had gotten quotes for replacing the carpet on our stairs with white oak, but the average estimate, not including materials, was $10,000 per flight. Three flights of stairs, at $10K per? Sounded like another job for me — except I had never remodeled stairs, and everyone I knew, including contractor friends, said I shouldn’t try. What really stressed me out was the fact I didn’t know what I didn’t know. It’s one thing to think you know how to do something and worry about whether you can actually pull it off; it’s even more stressful to know there are things you don’t know. So I stressed about it while we waited for the wood to arrive, and acclimate. Sound silly? On second thought, not really. Regardless of the worthiness of the cause, if you feel stressed, you feel stressed — and studies show four in five people experience stress at work. Including Jeff Bezos. Here’s Bezos in 2001 talking about stress: Stress primarily comes from not taking action over something that you can have some control over. If I find that some particular thing is causing me to have stress, that’s a warning flag for me. What it means is… something is bothering me that I haven’t yet taken action on. As soon as I identify it, and make the first phone call, or send the first email, or whatever we’re going to do to address that situation … even if it’s not solved, the mere fact we’re addressing it dramatically reduces any stress that might come from it. Stress comes from ignoring things you shouldn’t be ignoring. I wasn’t ignoring the stairs project (it felt like it was always lurking in the back of my mind), but I wasn’t doing anything to address the situation. So I removed the carpet from a couple of treads and risers so I could see the stringers underneath. I got a stair jig and figured out how to use it. I got some plywood and cut a few practice pieces to start to learn how to get the fit right. The problem wasn’t solved. The worry hadn’t gone away. But I felt a lot better about it, because taking action had helped me turn a few unknowns into knowns. I didn’t know everything I needed to know, but I was on a path to figuring it out. Science backs up that approach. A study published in Stress and Health found that having a plan not only improves outcomes, it also reduces stress. A study published in Healthcare found that active coping strategies reduce self-perceived stress. A study published in Journal of Behavioral Medicine found that taking action significantly reduces self-perceived stress, regardless of the outcome of those actions. Feel stressed? First, identify the source of your stress, and be specific. Maybe you’re worried about completing a project on time. Or concerned about a relationship. Or hesitant to speak up about a problem. Or unsettled by a recent conversation. Don’t settle for “I feel stressed.” Take the time to think about the reasons you feel the way you do. Then, don’t wait until you have the problem all figured out. (If I had done that, I never would have started working on our stairs.) Come up with one or two things you can do to start to address why you feel stressed. Then do those things. Action will lead to action, and each step along the way you’ll feel a little better about whatever situation you face. And a little less stressed. Science says so. —Jeff Haden This article originally appeared on Fast Company’s sister website, Inc.com. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy. View the full article

Here is a recap of what happened in the search forums today...View the full article

One of the most dependable ways to grow organic visibility was to publish more content. Expanding into the long tail and creating pages around different variations of a topic often led to steady traffic growth. Many SEO teams still operate with this mindset. Content calendars are built around search volume targets, and growth is often equated with how much new content is produced. The problem is the results no longer reflect the effort. In many cases, adding more pages doesn’t lead to increased visibility and can even dilute overall performance. Large content libraries are harder to maintain, compete internally, and often result in fewer pages surfacing in search results. The challenge is no longer producing more content, but understanding why much of it fails to contribute to visibility. Why content volume worked for SEO For a long time, increasing content volume was a rational and effective strategy. Search engines relied heavily on keyword matching and topical coverage, which meant expanding into the long tail created more opportunities to capture demand. Competition was also significantly lower, and many queries had limited high-quality results, so publishing across a wide range of keyword variations often led to quick visibility gains. In this environment, covering more topics translated directly into increased traffic. Publishing frequency also helped strengthen domain authority. Sites that consistently added new content signaled freshness and relevance, which improved their ability to compete in search results. This approach was further amplified by programmatic SEO. By creating scalable templates and targeting large keyword sets, companies generated thousands of pages and captured traffic at scale. Most importantly, this strategy worked because it aligned with how search engines evaluated content at the time. Expanding coverage increased the likelihood of ranking, and more pages meant more opportunities to be discovered. However, the conditions that made this approach effective have changed. As search ecosystems have evolved and competition has increased, the relationship between content volume and visibility has become less predictable. Dig deeper: Content marketing in an AI era: From SEO volume to brand fame Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with Why this model is breaking down Content saturation Most commercially relevant topics now have dozens of established pages competing for the same queries, many with years of accumulated links and behavioral data. A new page enters this environment at a disadvantage because the keyword spaces it targets are already consolidated around results with existing authority and signal history. Diminishing returns As sites expand into adjacent keyword variations, search engines increasingly route similar queries to the same URL rather than distributing traffic across multiple pages. This shows up in Google Search Console as two or three URLs splitting impressions on identical queries — neither ranking strongly because neither has consolidated authority. The intent overlap that content teams treat as coverage, Google treats as redundancy. Changes in search experience AI Overviews now appear across a significant and growing share of informational queries. Google has confirmed continued expansion of the feature across search types and markets. Informational content is the most affected by this shift, and it’s also the type most volume strategies produce. A site with a large number of blog articles is therefore more exposed than one focused on a smaller set of transactional pages. More ranked pages don’t produce proportional traffic when an increasing share of visible positions no longer generate a click. Indexing limits Google’s budget documentation states directly that low-value URLs drain crawl activity away from pages that matter. At scale, thin or redundant content is deprioritized — meaning a significant percentage of a site’s published pages may never meaningfully enter search competition regardless of how much continues to be added. Dig deeper: The authority era: How AI is reshaping what ranks in search The hidden mechanics behind content saturation What’s less understood is how content libraries behave at scale. These are system-level problems that compound over time and are difficult to reverse. Content debt Every page published creates an ongoing obligation. It needs to be monitored for ranking decay, updated when information changes, evaluated periodically for pruning or consolidation, and factored into crawl allocation. These costs are rarely accounted for at the point of creation. At low volumes, this is manageable. At scale, it becomes a compounding liability. A site with 2,000 articles isn’t sitting on 2,000 assets, it’s managing 2,000 maintenance commitments that depreciate at different rates. Editorial resources that could strengthen existing high-performing pages are instead absorbed by keeping a growing library from becoming a liability. The true cost of a volume-driven content strategy only becomes visible 18 to 24 months after the investment, when maintenance demands begin to outpace the capacity to meet them. Crawl inefficiency and cannibalization Google allocates a finite crawl budget to each domain. When a site scales content volume without proportional gains in quality or authority, Googlebot distributes that budget across a larger number of pages, many of which offer limited signal value. The result is that high-value pages are crawled less frequently, indexed less reliably, and are slower to reflect updates. This creates a compounding problem for sites with important transactional or evergreen pages that depend on frequent re-crawling to stay current and competitive. Beyond crawl distribution, similar pages targeting overlapping intent compete for the same ranking positions internally. Search engines consolidate these signals rather than rewarding each page individually, meaning two pages targeting near-identical queries often perform worse combined than one authoritative page targeting both would perform alone. Topical authority dilution Search engines evaluate whether a site is a genuinely deep and trustworthy resource within a defined topic space. Expanding into a wide range of loosely related subtopics can erode this signal rather than strengthen it. A site with 40 tightly interconnected, substantive pieces on a specific topic will consistently outperform one with 400 surface-level articles spread across adjacent themes. The depth and coherence of coverage within a defined area are what build the authority signal that drives durable rankings. Pursuing breadth at the expense of depth fragments that signal, making it harder for search engines to assign clear expertise to the domain on any individual topic, even the ones the site knows best. Weak content and behavioral signals Search engines use behavioral data such as dwell time, return-to-search rates, and click-through rates as quality signals at both the page and domain levels. When a site publishes high volumes of content that users engage with poorly, those signals accumulate and begin to affect how search engines evaluate the domain as a whole. This creates a negative reinforcement loop that’s difficult to detect and slow to reverse. Weak pages actively contribute to lower domain-level quality assessments, affecting the performance of pages that would otherwise rank well. More mediocre content compounds. Each low-engagement publish incrementally reduces the baseline trust that search engines extend to the domain’s better work. Get the newsletter search marketers rely on. See terms. The rise of citation-driven visibility The goal of SEO has traditionally been to rank. Increasingly, the more valuable outcome is to be cited or referenced in AI-generated summaries, pulled into knowledge panels, or sourced by other publishers as a primary reference. These two outcomes require fundamentally different content strategies. LLMs and AI Overviews are selective about which sources they draw from. The selection is weighted toward pages with strong E-E-A-T signals, high specificity, and clear authoritativeness within a defined domain. A site that has published hundreds of generic articles covering a topic broadly is less likely to be treated as a primary source than a site that has published fewer, more definitive pieces with clear depth and original perspective. Volume doesn’t increase citation probability — it may actively reduce it by signaling that the domain is a generalist content producer rather than a reliable primary reference. The long tail is saturated The accessible long tail that drove content volume strategies for the better part of a decade no longer exists in the same form. Between 2010 and 2020, there were genuinely underserved keyword opportunities across most industries. Today, in most commercial verticals, every remotely valuable query has multiple established pages competing for it, especially from high-authority domains with years of accumulated signals. New content entering this environment doesn’t find open space. It enters a war of attrition against incumbents with advantages it can’t easily overcome. The marginal SEO return on a new article targeting a long-tail keyword is a fraction of what it was five years ago. The economics only justify creation when there’s a genuinely differentiated angle, a proprietary data point, or a perspective that exists on your page that other pages can’t offer. A keyword existing is no longer a sufficient reason to publish. At scale, these factors turn content growth into diminishing returns rather than compounding gains. The library becomes harder to maintain, harder for search engines to evaluate clearly, and harder to extract meaningful visibility from — regardless of how much is added to it. Dig deeper: How to keep your content fresh in the age of AI How to shift from content volume to impact The implication is to change what publishing is for. Volume targets made sense when more pages meant more opportunities. In the current environment, they measure the wrong thing. The more useful question isn’t how much content a team is producing, but how much of what already exists is actively contributing to visibility, and what is quietly working against it. For most sites, that audit reveals the same pattern. A relatively small number of pages generate the majority of organic traffic. A larger number generates little to none, and a significant portion actively drains crawl allocation, fragments topical authority, or dilutes the behavioral signals that stronger pages depend on. You need to move from expansion to consolidation. Existing pages that cover overlapping intent are stronger merged than competing. Thin pages that rank for nothing and engage no one are more valuable removed than retained. The energy going into producing new content at volume is often better spent deepening the pages that already have authority and signal history behind them. New content earns its place when it: Addresses something genuinely unaddressed. Offers a perspective that existing pages can’t. Targets an intent the site currently lacks. In practice, this means retiring a few default assumptions: That publishing for every keyword variation is coverage. That indexing is the same as performance. That output volume is a proxy for strategic progress. None of these were ever true measures of content effectiveness. They were convenient ones. Dig deeper: Content strategy in 2026: What actually changed (and what didn’t) A new model for content-driven growth The replacement for volume isn’t simply better content. It’s a different definition of what content is trying to achieve. Depth over breadth Focus coverage on a smaller number of topics and develop them thoroughly. A single piece that addresses a topic with specificity, original perspective, and clear authorial expertise will outperform multiple pieces covering adjacent variations of the same theme. Depth is what builds authority signals, drives engagement, and increases citation potential. Prioritize what the site can say with the most credibility. Distribution as a multiplier Allocate more effort to distribution. Publishing less creates capacity to deliver strong content to the right audiences. Distribution is a core part of SEO performance in a citation-driven environment. Being citation-worthy Create content that can serve as a primary source. Focus on clear points of view, verifiable expertise, and specific insights that other pages can’t replicate. The goal is to be referenced in AI-generated summaries, cited by other publishers, and included in the knowledge systems search engines rely on. Dig deeper: Content alone isn’t enough: Why SEO now requires distribution See the complete picture of your search visibility. Track, optimize, and win in Google and AI search from one platform. Start Free Trial Get started with The uncomfortable truth Sites that rely on frequency and broad coverage are being outperformed by sites that are clearly authoritative on a defined topic, consistently useful to a specific audience, and structured in a way that search systems can evaluate with confidence. Prioritize depth, clarity of expertise, and consistency within a focused topic area. Treat each published page as a long-term asset that requires ongoing maintenance, evaluation, and improvement. The content factory model is no longer effective. The approach that replaces it requires more effort, stronger editorial standards, and a higher bar for what gets published. View the full article

We’re in our optimization era: Increasingly connected, efficient, and, perhaps unsurprisingly, incapable of giving anything our full attention. But I don’t want to be optimized anymore. Algorithms predict what we’ll watch, AI generates what we’ll read, and marketing systems are built specifically to remove friction from discovery to purchase. Feeds blur together, and messages feel interchangeable. Connection—the thing marketing is supposed to create—has become exponentially harder to achieve. We need to bring the friction back, and that doesn’t come from obsessing over scale. Connection isn’t about reaching everyone at once; it’s about showing up meaningfully in the communities that matter most. “Chasing the fandom and not the random,” as Julia Alexander recently said on The Grill Room. A FOCUS ON TRUST The brands breaking through today aren’t optimizing for volume; they’re building networks of trust. One audience, one voice, and one relationship at a time. Scale, as it turns out, is the byproduct of connection, not the strategy. The industry’s current obsession with scaling connection misses the point. When brands treat connection like a growth metric, it’s a sign the audience has become abstract. You earn connection at the individual level when the right brand leverages the right voice for the right audience—the same way relationships work in real life. Instead of gobbling up creators or cycling through the same handful on repeat, a more networked, web-like approach to casting and partnerships is the smarter way to grow brand trust. And that is, of course, how you reach more audiences and earn broad awareness, which is, well, the good kind of scale. Brands like Loewe and Jacquemus, two luxury fashion brands, are prime examples of this approach. Each pursued very specific strategies, leveraging creators and creatives with genuine connections to their respective brands. Each followed their north star, holding steady on a clearly defined path, which eventually earned the attention every brand craves right now. Regardless of revenue (booming at Loewe, not so much at Jacquemus), these brands are some of the most commonly referenced; they’re cultural juggernauts. WHO MATTERS MORE THAN HOW MANY Twenty years ago, there were a handful of publications in which you’d publish an ad or seed a story to capture specific eyeballs. WWD for fashion, or The Wall Street Journal for business or opinion. Today, when Instagram and Substack are the new front page, it could take thousands of voices to reach the same number of people. It could, of course, take just one. The answer is likely somewhere in the middle, but it’s the who that matters. Not the how many. The question should not be how to scale a connection, but how to slow down enough to actually create one in the first place. Today’s winning brands aren’t flooding the feed; they’re intentionally engaging their specific audience(s). They understand that they earn through relevance, care, and a little bit of friction. The brands that dare to be deliberate, to stand out, are the ones that actually connect, and the ones we remember. Josh Rosenberg is the cofounder and CEO of Day One Agency. View the full article

Oprah Winfrey’s podcast is headed to Amazon. Winfrey’s production company, Harpo Entertainment, struck a multiyear deal to give Amazon-owned Wondery exclusive distributing and advertising rights to “The Oprah Podcast,” the companies announced Monday. Under the agreement, Winfrey’s podcast will expand to two new episodes a week starting this summer — and Wondery will distribute the show’s audio and video across Amazon platforms. Under the deal, Amazon has also obtained rights to the library of the widely-watched “The Oprah Winfrey Show” — which ran from 1986 to 2011 — as well as the talk show host’s book club and “Favorite Things” franchises. No financial terms of the agreement were immediately shared. In recent years, Winfrey also has had partnerships with Apple and Starbucks. Her new agreement could anger independent booksellers who regard Amazon as their primary competitor. A spokesperson for the trade group the American Booksellers Association did not immediately respond Monday to a request for comment. A spokesperson for Harpo shared a statement with The Associated Press that “‘Oprah’s Book Club’ will continue to support books wherever they are sold.” Winfrey’s podcast joins a lineup of other celebrity-led shows now at Amazon. In 2024, for example, Wondery similarly reached an exclusive distribution and advertising deal for “New Heights” — a podcast from Chiefs tight end Travis Kelce and his brother, former Eagles center Jason Kelce. Winfrey launched “The Oprah Podcast” in December 2024. In a prepared statement Monday, Winfrey said that hosting the show “allows me to continue the work I feel called to do – opening the door for conversations that matter.” She added that expanding its reach “is an opportunity I embrace.” Wondery will begin distributing “The Oprah Podcast” across Amazon services like Prime Video, Amazon Music, Fire TV Channels and Audible in July, according to Monday’s announcement. Winfrey’s podcast will also continue to be available on YouTube and other popular platforms. —Associated Press View the full article