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Search is no longer a blue-links game. Discovery increasingly happens inside AI-generated answers – in Google AI Overviews, ChatGPT, Perplexity, and other LLM-driven interfaces. Visibility isn’t determined solely by rankings, and influence doesn’t always produce a click. Traditional SEO KPIs like rankings, impressions, and CTR don’t capture this shift. As search becomes recommendation-driven and attribution grows more opaque, SEO needs a new measurement layer. LLM consistency and recommendation share (LCRS) fills that gap. It measures how reliably and competitively a brand appears in AI-generated responses – serving a role similar to keyword tracking in traditional SEO, but for the LLM era. Why traditional SEO KPIs are no longer enough Traditional SEO metrics are well-suited to a model where visibility is directly tied to ranking position and user interaction largely depends on clicks. In LLM-mediated search experiences, that relationship weakens. Rankings no longer guarantee that a brand appears in the answer itself. A page can rank at the top of a search engine results page yet never appear in an AI-generated response. At the same time, LLMs may cite or mention another source with lower traditional visibility instead. This exposes a limitation in conventional traffic attribution. When users receive synthesized answers through AI-generated responses, brand influence can occur without a measurable website visit. The impact still exists, but it isn’t reflected in traditional analytics. At the core of this change is something SEO KPIs weren’t designed to capture: Being indexed means content is available to be retrieved. Being cited means content is used as a source. Being recommended means a brand is actively surfaced as an answer or solution. Traditional SEO analytics largely stop at indexing and ranking. In LLM-driven search, the competitive advantage increasingly lies in recommendation – a dimension existing KPIs fail to quantify. This gap between influence and measurement is where a new performance metric emerges. 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 LCRS: A KPI for the LLM-driven search era LLM consistency and recommendation share is a performance metric designed to measure how reliably a brand, product, or page is surfaced and recommended by LLMs across search and discovery experiences. At its core, LCRS answers a question traditional SEO metrics can’t: When users ask LLMs for guidance, how often and how consistently does a brand appear in the answer? This metric evaluates visibility across three dimensions: Prompt variation: Different ways users ask the same question. Platforms: Multiple LLM-driven interfaces. Time: Repeatability rather than one-off mentions. LCRS isn’t about isolated citations, anecdotal screenshots, or other vanity metrics. Instead, it focuses on building a repeatable, comparative presence. That makes it possible to benchmark performance against competitors and track directional change over time. LCRS isn’t intended to replace established SEO KPIs. Rankings, impressions, and traffic still matter where clicks occur. LCRS complements them by covering the growing layer of zero-click search – where recommendation increasingly determines visibility. Dig deeper: Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it Breaking down LCRS: The two components LCRS has two main components: LLM consistency and recommendation share. LLM consistency In the context of LCRS, consistency refers to how reliably a brand or page appears across similar LLM responses. Because LLM outputs are probabilistic rather than deterministic, a single mention isn’t a reliable signal. What matters is repeatability across variations that mirror real user behavior. Prompt variability is the first dimension. Users rarely phrase the same question in exactly the same way. High LLM consistency means a brand surfaces across multiple, semantically similar prompts, not just one phrasing that happens to perform well. For example, a brand may appear in response to “best project management tools for startups” but disappear when the prompt changes to “top alternatives to Asana for small teams.” Temporal variability reflects how stable those recommendations are over time. An LLM may recommend a brand one week and omit it the next due to model updates, refreshed training data, or shifts in confidence weighting. Consistency here means repeated queries over days or weeks produce comparable recommendations. That indicates durable relevance rather than momentary exposure. Platform variability accounts for differences between LLM-driven interfaces. The same query may yield different recommendations depending on whether a conversational assistant, an AI-powered search engine, or an integrated search experience responds. A brand demonstrating strong LLM consistency appears across multiple platforms, not just within a single ecosystem. Consider a B2B SaaS brand that different LLMs consistently recommend when users ask for “CRM tools for small businesses,” “CRM software for sales teams,” and “HubSpot alternatives.” That repeatable presence indicates a level of semantic relevance and authority LLMs repeatedly recognize. Recommendation share While consistency measures repeatability, recommendation share measures competitive presence. It captures how frequently LLMs recommend a brand relative to other brands in the same category. Not every appearance in an AI-generated response qualifies as a recommendation: A mention occurs when an LLM references a brand in passing, for example, as part of a broader list or background explanation. A suggestion positions the brand as a viable option in response to a user’s need. A recommendation is more explicit, framing the brand as a preferred or leading choice. It’s often accompanied by contextual justification such as use cases, strengths, or suitability for a specific scenario. When LLMs repeatedly answer category-level questions such as comparisons, alternatives, or “best for” queries, they consistently surface some brands as primary responses while others appear sporadically or not at all. Recommendation share captures the relative frequency of those appearances. Recommendation share isn’t binary. Appearing among five options carries less weight than being positioned first or framed as the default choice. In many LLM interfaces, response ordering and emphasis implicitly rank recommendations, even when no explicit ranking exists. A brand that consistently appears first or includes a more detailed description holds a stronger recommendation position than one that appears later or with minimal context. Recommendation share reflects how much of the recommendation space a brand occupies. Combined with LLM consistency, it provides a clearer picture of competitive visibility in LLM-driven search. To be useful in practice, this framework must be measured in a consistent and scalable way. Dig deeper: What 4 AI search experiments reveal about attribution and buying decisions How to measure LCRS in practice Measuring LCRS demands a structured approach, but it doesn’t require proprietary tooling. The goal is to replace anecdotal observations with repeatable sampling that reflects how users actually interact with LLM-driven search experiences. 1. Select prompts The first step is prompt selection. Rather than relying on a single query, build a prompt set that represents a category or use case. This typically includes a mix of: Category prompts like “best accounting software for freelancers.” Comparison prompts like “X vs. Y accounting tools.” Alternative prompts like “alternatives to QuickBooks.” Use-case prompts like “accounting software for EU-based freelancers.” Phrase each prompt in multiple ways to account for natural language variation. 2. Confirm tracking Next, decide between brand-level and category-level tracking. Brand prompts help assess direct brand demand, while category prompts are more useful for understanding competitive recommendation share. In most cases, LCRS is more informative at the category level, where LLMs must actively choose which brands to surface. 3. Execute prompts and collect data Tracking LCRS quickly becomes a data management problem. Even modest experiments involving a few dozen prompts across multiple days and platforms can generate hundreds of observations. That makes spreadsheet-based logging impractical. As a result, LCRS measurement typically relies on programmatically executing predefined prompts and collecting the responses. To do this, define a fixed prompt set and run those prompts repeatedly across selected LLM interfaces. Then parse the outputs to identify which brands are recommended and how prominently they appear. 4. Analyze the results You can automate execution and collection, but human review remains essential for interpreting results and accounting for nuances such as partial mentions, contextual recommendations, or ambiguous phrasing. Early-stage analysis may involve small prompt sets to validate your methodology. Sustainable tracking, however, requires an automated approach focused on a brand’s most commercially important queries. As data volume increases, automation becomes less of a convenience and more of a prerequisite for maintaining consistency and identifying meaningful trends over time. Track LCRS over time rather than as a one-off snapshot because LLM outputs can change. Weekly checks can surface short-term volatility, while monthly aggregation provides a more stable directional signal. The objective is to detect trends and identify whether a brand’s recommendation presence is strengthening or eroding across LLM-driven search experiences. With a way to track LCRS over time, the next question is where this metric provides the most practical value. Get the newsletter search marketers rely on. See terms. Use cases: When LCRS is especially valuable LCRS is most valuable in search environments where synthesized answers increasingly shape user decisions. Marketplaces and SaaS Marketplaces and SaaS platforms benefit significantly from LCRS because LLMs often act as intermediaries in tool discovery. When users ask for “best tools,” “alternatives,” or “recommended platforms,” visibility depends on whether LLMs consistently surface a brand as a trusted option. Here, LCRS helps teams understand competitive recommendation dynamics. Your money or your life In “your money or your life” (YMYL) industries like finance, health, or legal services, LLMs tend to be more selective and conservative in what they recommend. Appearing consistently in these responses signals a higher level of perceived authority and trustworthiness. LCRS can act as an early indicator of brand credibility in environments where misinformation risk is high and recommendation thresholds are stricter. Comparison searches LCRS is also particularly relevant for comparison-driven and early-stage consideration searches. LLMs often summarize and narrow choices when users explore options or seek guidance before forming brand preferences. Repeated recommendations at this stage influence downstream demand, even if no immediate click occurs. In these cases, LCRS ties directly to business impact by capturing influence at the earliest stages of decision-making. While these use cases highlight where LCRS can be most valuable, it also comes with important limitations. Dig deeper: How to apply ‘They Ask, You Answer’ to SEO and AI visibility Limitations and caveats of LCRS LCRS is designed to provide directional insight, not absolute certainty. LLMs are inherently nondeterministic, meaning identical prompts can produce different outputs depending on context, model updates, or subtle changes in phrasing. As a result, you should expect short-term fluctuations in recommendations and avoid overinterpreting them. LLM-driven search experiences are also subject to ongoing volatility. Models are frequently updated, training data evolves, and interfaces change. A shift in recommendation patterns may reflect platform-level changes rather than a meaningful change in brand relevance. That’s why you should evaluate LCRS over time and across multiple prompts rather than as a single snapshot. Another limitation is that programmatic or API-based outputs may not perfectly mirror responses generated in live user interactions. Differences in context, personalization, and interface design can influence what individual users see. However, API-based sampling provides a practical, repeatable reference point because direct access to real user prompt data and responses isn’t possible. When you use this method consistently, it allows you to measure relative change and directional movement, even if it can’t capture every nuance of user experience. Most importantly, LCRS isn’t a replacement for traditional SEO analytics. Rankings, traffic, conversions, and revenue remain essential for understanding performance where clicks and user journeys are measurable. LCRS complements these metrics by addressing areas of influence that currently lack direct attribution. Its value lies in identifying trends, gaps, and competitive signals, not in delivering precise scores or deterministic outcomes. Viewed in that context, LCRS also offers insight into how SEO itself is evolving. 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 What LCRS signals about the future of SEO The introduction of LCRS reflects a broader shift in how search visibility is earned and evaluated. As LLMs increasingly mediate discovery, SEO is evolving beyond page-level optimization toward search presence engineering. The objective is no longer ranking individual URLs. Instead, it’s ensuring a brand is consistently retrievable, understandable, and trustworthy across AI-driven systems. In this environment, brand authority increasingly outweighs page authority. LLMs synthesize information based on perceived reliability, consistency, and topical alignment. Brands that communicate clearly, demonstrate expertise across multiple touchpoints, and maintain coherent messaging are more likely to be recommended than those relying solely on isolated, high-performing pages. This shift places greater emphasis on optimization for retrievability, clarity, and trust. LCRS doesn’t attempt to predict where search is headed. It measures the early signals already shaping LLM-driven discovery and helps SEOs align performance evaluation with this new reality. The practical question for SEOs is how to respond to these changes today. The shift from position to presence As LLM-driven search continues to reshape how users discover information, SEO teams need to expand how they think about visibility. Rankings and traffic remain important, but they no longer capture the full picture of influence in search experiences where answers are generated rather than clicked. The key shift is moving from optimizing only for ranking positions to optimizing for presence and recommendation. LCRS offers a practical way to explore that gap and understand how brands surface across LLM-driven search. The next step for SEOs is to experiment thoughtfully by sampling prompts, tracking patterns over time, and using those insights to complement existing performance metrics. View the full article

It’s the Thursday “ask the readers” question. A reader writes: I live in a lovely touristy small city with a university. It’s a great place to live, with lots of services and things to do for its size. I have a job in my field which I still enjoy in some ways, but I’ve been in it for 10 years and am terribly bored. I’ve really pushed the boundaries of my position and am feeling so stuck. I’ve been actively applying in town for three years. It’s rare that positions come up, and when they do, they are inundated with candidates. Our city is known for having a “scenery tax” and having wildly educated baristas. So in the past year, I’ve started applying to positions in different states and have received two offers, but I’d go to visit and they just didn’t seem like as nice of places to live, so I turned them down. I don’t know what to do. Part of me feels selfish for thinking about moving. We have a great community and I have two little kids who are doing well here. But I get really sad when I think about this job being my whole career. I know people living here face this problem all the time. I’d love to hear from folks who either stayed or went and how it shook out. Readers, please share your thoughts in the comment section. The post ask the readers: should I leave a great city that I love to expand my job options? appeared first on Ask a Manager. View the full article

When Sergey Brin spoke at Stanford University’s school of engineering centennial celebration recently, the Google co-founder was open about his career mistakes. “When you have your cool new wearable device idea, really fully bake it before you have a cool stunt involving skydiving and airships,” he joked, referring to the infamous Google Glass flop. But one misstep he admitted to might surprise a lot of people who dream of the day they can quit their 9-to-5. “I actually retired like a month before Covid hit, and it was the worst decision,” Brin said. He was such a failure at retirement that he has since returned to everyday work at Google, spearheading its efforts to catch up in the AI race. Going back to work just for fun might sound like a uniquely billionaire move. But a stack of research suggests that Brin’s dissatisfaction in retirement and subsequent decision to return to work isn’t that uncommon. His story contains an essential but often overlooked lesson that can help anyone better plan their retirement. Why Sergey Brin unretired Like many people, Brin had a relaxing vision for his post-working life. “I was gonna sit in cafés and study physics, which was my passion at the time,” he told the Stanford audience. Fate intervened in the form of Covid. But Brin wasn’t dissatisfied with his retirement just because he was locked in his house all day. “I was just kind of stewing and felt myself spiraling, not being sharp,” he recalled. After the Google offices partially reopened, he started going in occasionally. Eventually, he “started spending more and more time on what later became called Gemini, which is super-exciting. To be able to have that technical creative outlet, I think that’s very rewarding, as opposed to if I’d stayed retired. I think that would’ve been a big mistake,” he added. Retirement struggles aren’t just for billionaires Brin’s issues with retirement are his own. More people dream of days on the golf course than pouring over physics textbooks. But Brin’s feelings of listlessness and intellectual decline are not at all exclusive to billionaires. When researchers from European business school Insead surveyed entrepreneurs who had gone through a big exit and become financially independent, they discovered many decided to retire. And many soon regretted it. “It is perfectly normal to discover that life post-financial freedom isn’t as happy as one might have expected it to be,” the researchers summed up. It’s not just restless entrepreneurs. Another recent study of retired Japanese salarymen revealed similar patterns. Having given so much of themselves to their careers, they often felt unmoored and purposeless when they left their jobs. Their retirement was “characterized by boredom—having nowhere to go to or having nothing to do. The sense of boredom led to a sense of isolation and low confidence in old age,” explained study author Shiori Shakuto. Adherents of the popular financial independence, retire early (FIRE) movement scrimp and sacrifice to retire early. Only for many of them to discover their dream of post-work life does not match reality. Several have written about the experience. “If you’ve spent decades in a career working 40 hours a week, it’s hard to suddenly stop working. Many early retirees feel uncomfortable feeling unproductive. As a result, they unretire to work on something meaningful. … It’s easy to get bored with 40 hours of extra free time a week,” wrote ex-FIRE early retiree Sam Dogen in one such blog post. A good retirement isn’t all about money All of this evidence, as well as Sergey Brin’s experience, point in the same direction. We tend to think of a successful retirement as a numbers game. If you save enough to be comfortable and indulge in whatever activities you enjoy, the end of working life should represent the start of the golden years. But all the people involved in these studies were set financially. Brin has a net worth north of $200 billion. Clearly, money is not the issue. The problem is purpose. As Brin’s fellow billionaire Bill Gates recently wrote: “As life expectancies go up, many people are living for years and even decades after they stop working. That sounds like a luxury, and it is in a lot of ways—but it is also a lot of time to fill.” Gates fills his time with philanthropy. Brin is back to building AI. The rest of us will probably not spend our post-work years doing anything as grand. But the same truth applies. If you think only about finances and not enough about how to meaningfully fill your days in retirement, you’re probably not going to enjoy yourself much. You also might, in Brin’s words, feel “less sharp.” Science has shown having purpose helps stave off dementia as well as boosts happiness. Sergey Brin’s lesson for the rest of us This doesn’t mean we should all work until we drop, of course. Instead, experts insist the essential takeaway is the need to plan for meaning as well as money. “It’s never too early, or too late, to start thinking about what you would want to do after achieving financial freedom. What would you do with your money and time?” the Insead researchers ask. So the next time you check the balance of your retirement savings account, take a moment to think not just about how much you will save, but also how you will spend your time. As Sergey Brin’s unretirement reminds us, even billions of dollars can’t guarantee you a good retirement if you don’t plan for purpose in your post-work life, too. —Inc. 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

They say job hunting is just like dating. Some are taking that advice literally. “Job market so bad I’m using Hinge to find work,” one job seeker posted on TikTok in December. Sharing a look at her dating app profile, in place of a photo of her best angle, she instead uploaded a snapshot of her résumé. Answering the prompt “a life goal of mine,” she wrote “to find work in the creative industries.” Since it was posted in December, the video has gained almost a quarter of a million views. In a recent update, the TikTok user shared that Hinge has since taken down her profile for breaking their policies. But she is not the only one. Others are also using this unconventional method to get their profiles in front of hiring managers. One claimed to land a six-figure job from a match on Bumble. “Sometimes I use hinge to match with people in my career field and ask if they’re hiring,” another posted. “It’s called being resourceful, innovative and bold,” they wrote in the caption. As sites like LinkedIn are overwhelmed with applications and employers rely on AI résumé screeners, applicants are finding creative ways to get their foot in the door. In a recent Glassdoor community pool, 29% of respondents said that they were using or considered using dating apps for career purposes. While networking on dating apps isn’t new, it appears to be a growing trend. A ResumeBuilder.com survey of about 2,200 U.S. dating site customers in October also found a third of dating app users had used the platforms for job or career-related purposes in the past year. Nearly one in 10 say it was the primary reason they used dating apps, with the most common platforms being Tinder, Bumble, and Facebook Dating. It’s not just those hoping to break into entry-level positions. Almost half of those using dating apps for job-related purposes reported incomes of more than $200,000. For many, the strategy has paid off—43% say they gained mentorship or career advice from networking on the apps, while 39% landed an interview, 37% received a referral or lead, and 37% received a job offer. One survey participant called the new job-hunting practice “weird but effective,” while another said, “It worked, but you need the audacity to ask.” Of course, the lines quickly become blurred when seeking employment in an environment designed for hookups and romantic pursuits. Especially if there’s a power balance at play. But desperate times call for desperate measures. It now takes more than 23 weeks on average for an unemployed person in the U.S. to find a new job. For one in four unemployed people, or 1.8 million Americans, they are still job hunting six months later. Long-term unemployment is now at its highest level in three years. Under these circumstances, it’s no surprise job seekers are turning to any means necessary to find new connections. And hey, it’s better than the inverse: anyone using LinkedIn as a personal dating pool. View the full article

The annual NFL tradition of firing the head coach as the season ends continues. This year, 10 top coaches got the axe, a staggering 31% of all NFL coaches. And they include football legends like John Harbaugh, after 18 seasons with the Baltimore Ravens, and Sean McDermott, who took the Buffalo Bills to the playoffs in eight out of nine seasons. Firing the head coach—just like firing the CEO in the business world—is the easy answer, and it looks good in the media: decisive, forward-looking, taking action. But, most times, this act alone falls short of fixing the problems that contributed to an organization’s failures. PART OF A SYSTEM In reality, the CEO is part of a system, and it’s the system that matters. You can have a B player CEO with a great team and board and deliver significant performance and culture gains. Alternatively, you can have an A player CEO with a weak board and team and fail spectacularly. If you only focus on “fixing the CEO,” you’re not focused on the right problem and can’t get to the right solution. Yet CEO turnover is at its highest level in more than a decade, according to a 2026 Spencer Stuart study reported in The Wall Street Journal. In fact, approximately one in nine CEOs were replaced in 1,500 large companies in 2025, including the CEOs of Disney, HP, Lululemon, PayPal and Procter & Gamble. Disney illustrates the downside of this. Just ask Bob Chapek. Sure, he had a rough three years as CEO of Walt Disney Co. before the board summarily fired him and brought back his predecessor, Bob Iger. Disney stock, at $125 a share when Chapek took over in February 2020, had fallen almost 40% to $90 by the time he got the axe on November 20, 2022. Iger arguably is one of the best CEOs in decades, and he rebuilt the company with incredibly successful acquisitions (Pixar, Marvel Entertainment, Star Wars, the Muppets). But his two years back at the top were less than stellar: Disney shares are up 17% since he took over, while the S&P media and entertainment index rose 99% in the same period. Obviously, Chapek alone wasn’t the problem, just as Iger alone wasn’t the solution. Rarely is the executive at the very top solely responsible for what went wrong. It owes to a multitude of weaknesses: illogical organization models, conflicting agendas, turf battles, reporting structures that don’t align with the company strategy, and communication lapses. There is rarely an objective assessment done ensuring the board is aligned with a new CEO or a new market entry for what success looks like, and the structures and talent required to achieve that success. This is especially true in the unforgiving and bottom-line-obsessed world of private equity (PE). The biggest myth in PE (and pro football) is that if you get the CEO right, and you get the strategy right, you will get the numbers you want on the scoreboard. Every CEO is encumbered by their surroundings. A PE board is possibly 50% of the CEO’s success or failure, and in my experience, a lack of alignment between how each part defines success is a root issue. Leaders of PE-funded businesses must also operate under very compressed timeframes that leave little room for exiling and replacing a CEO. By the time the CEO has been exiled, it can be even harder—or too late—to drive a successful outcome. A TEAM APPROACH This is why, again, even B player CEOs with strong teams and supportive boards find success, while A-rated commanders often falter with the wrong organization structure and fractured boards. The CEO is but one part of a whole system that must play well together, including the board, key team members, business partners, core customers, and suppliers. Yet highly intelligent and competitive people often miss their biggest and most controllable opportunity to ensure their CEO is positioned for success. That is to better manage their own decision-making, accountability, and communication as board members and teammates and ensure the organization is designed for success. Alice Mann is founder and CEO of Mann Partners. View the full article

AI is helping teams build software and tools faster than ever—but that doesn’t mean we’re building smarter. I’ve seen entire prototypes spin up in a day, thanks to AI coding assistants. But when you ask how they were built, or whether they’re secure, you get a lot of blank stares. That’s the gap emerging now, between what’s possible with AI, and what’s actually ready to scale. What looks like progress can quickly become a liability. Especially when no one’s quite sure how the thing was built in the first place. Before you go all-in on AI-assisted coding, check these five fault lines: 1. You can’t govern what you can’t see. Perhaps the most overlooked risk of AI-assisted coding isn’t technical, it’s operational. In the rush to deploy AI tools, many companies have unintentionally created a layer of “shadow engineering.” Developers use these tools without official policies or visibility, leaving leaders in the dark about what’s being built and how. As Mark Curphey, cofounder of Crash Override, told me: “AI is accelerating everything. But without insight into what’s being built, by whom, or where it’s going, you’re scaling chaos with no controls.” That’s why visibility can’t be an afterthought; it’s what makes both governance and acceleration possible. Platforms like Crash Override are designed to surface how AI is being used across the engineering org, offering a real-time view into what’s being generated, where it’s going, and whether it’s introducing risk or value. And that visibility doesn’t exist in isolation. Tools like Jellyfish help connect development work to business goals, while Codacy monitors code quality. But none of these tools can do their job well if you don’t know what’s happening under the hood. Visibility isn’t about surveillance, it’s about building on a solid foundation. 2. Productivity is up. So is your risk exposure. A 2025 study Apiiro, an application security firm, found that AI-assisted developers are shipping 3 to 4 times more code with GenAI tools. But they’re also generating 10 times more security risks. These weren’t just syntax errors. The increase included hidden access risks, insecure code patterns, exposed credentials, and deep architectural flaws—issues far more complex and costly to resolve over time. 3. AI-generated code is a potential legal risk. Because AI coding tools are trained on vast libraries of public code, they can generate snippets governed by restrictive open-source licenses. That raises important compliance questions, especially with licenses like GPL or AGPL, which could, in theory, require companies to open-source any software built on top of that output. But it’s worth clarifying: No company has been sued (yet) for using AI-generated code. The lawsuits we’ve seen (like the GitHub Copilot class action) have targeted the AI toolmakers, not the teams using their output. And the majority of GitHub’s claims were ultimately thrown out. Still, this is a fast-evolving area with real implications. Auditboard’s 2025 study found that 82% of enterprise organizations were already deploying AI tools, but only 25% report having any sort of official governance in place. That disconnect may not be a courtroom issue today, but it’s a visibility and audit issue that leaders can’t afford to ignore. 4. Speed is great, until only one person knows how it works. The “bus factor” has long described a worst-case scenario: What happens if the one person who knows how your software works suddenly disappears? “Powered by AI, an average developer becomes 100 times more productive. A superstar becomes 1,000 times,” Curphey noted. “Now imagine two of them are pushing all of that code into production. If they disappear, the company’s in serious trouble.” But the goal isn’t zero risk—it’s coverage. Just like test cases help ensure software is resilient, teams need to ensure knowledge and ownership are distributed. That includes understanding who’s building what, where the AI is involved, and how those systems will be maintained over time. Ironically, GenAI can help with this. It can surface patterns, identify gaps, and map ownership in ways traditional tooling can’t. More than just a productivity boost, it can be a tool for reducing fragility across your team and your codebase. 5. It’s easy to end up with “software slop.” Good, scalable AI-assisted code starts with the prompt. AI will generate exactly what you ask for. But if you don’t fully understand the technical constraints, or the risks you’re overlooking, it might give you code that looks good but has critical flaws in security or performance under the hood. You certainly don’t have to be a developer to use these tools well. But you do need to know what you don’t know, and how to account for it. As Curphey notes in a company blog post, “If you wouldn’t accept that level of vagueness from a junior engineer, why would you accept it from yourself when prompting?” Otherwise, you’re moving fast and creating a kind of digital brain rot: systems that degrade over time because no one really understands how they were built. FROM VIBE CHECK TO REALITY CHECK The takeaway: AI may accelerate output, but it also accelerates risk. Without rigorous review and governance, you may be shipping code that functions, but isn’t structurally sound. So while AI is changing how software gets built, we need to be sure we’re building on a solid foundation. It’s no longer enough to move fast or ship often. As leaders, we need to understand how AI is being used inside our teams, and whether the things getting built are actually stable, scalable, and secure. Because if you don’t know what your team is using AI to build today, you may not like what you’re shipping tomorrow. Lisa Larson-Kelley is founder and CEO of Quantious. View the full article

Indian Prime Minister Narendra Modi on Thursday pitched India as a central player in the global artificial intelligence ecosystem, saying the country aims to build technology at home while deploying it worldwide. “Design and develop in India. Deliver to the world. Deliver to humanity,” Modi told a gathering of some world leaders, technology executives and policymakers at the India AI Impact Summit in New Delhi. Modi’s remarks came as India — one of the fastest-growing digital markets — seeks to leverage its experience in building large-scale digital public infrastructure and to present itself as a cost-effective hub for AI innovation. The summit was also addressed by French President Emmanuel Macron, Google CEO Sundar Pichai and U.N. Secretary-General António Guterres, who called for a $3 billion fund to help poorer countries build basic AI capacity, including skills, data access and affordable computing power. “The future of AI cannot be decided by a handful of countries, or left to the whims of a few billionaires,” Guterres said, stressing that AI must “belong to everyone.” India aims to ramp up its AI scale India is using the summit to position itself as a bridge between advanced economies and the Global South. Indian officials cite the country’s digital ID and online payments systems as a model for deploying AI at low cost, particularly in developing countries. “We must democratize AI. It must become a tool for inclusion and empowerment, particularly for the Global South,” Modi said. With nearly 1 billion internet users, India has become a key market for global technology companies expanding their AI businesses. Last December, Microsoft announced a $17.5 billion investment over four years to expand cloud and AI infrastructure in India. It followed Google’s $15 billion investment over five years, including plans for its first AI hub in the country. Amazon has also pledged $35 billion by 2030, targeting AI-driven digitization. India is also seeking up to $200 billion in data center investment in the coming years. The country, however, lags in developing its own large-scale AI model like U.S.-based OpenAI or China’s DeepSeek, highlighting challenges such as limited access to advanced semiconductor chips, data centers and hundreds of local languages to learn from. The summit has faced troubles The summit opened Monday with organizational glitches, as attendees and exhibitors reported long lines and delays, and some complained on social media that personal belongings and display items had been stolen. Organizers later said the items were recovered. Problems resurfaced Wednesday when a private Indian university was expelled from the summit after a staff member showcased a commercially available Chinese-made robotic dog while claiming it as the institution’s own innovation. The setbacks continued Thursday when Microsoft co-founder Bill Gates withdrew from a scheduled keynote address. No reason was given, though the Gates Foundation said the move was intended “to ensure the focus remains on the AI Summit’s key priorities.” Gates is facing questions over his ties to late sex offender Jeffrey Epstein. —Associated Press View the full article

Oracle has rolled out significant enhancements to its Fusion Cloud Supply Chain & Manufacturing platform, specifically aimed at aiding small to midsize businesses engaged in process manufacturing. This move comes as organizations are increasingly pressured to achieve operational excellence while adhering to stringent regulatory standards. In sectors such as life sciences, chemicals, and food and beverage, manufacturers grapple with the challenge of delivering consistent quality amid variations in materials, yields, and production processes. The new features from Oracle are designed to tackle these issues head-on, enabling businesses to gain real-time visibility into their production workflows, thus ensuring greater compliance and quality assurance. Derek Gittoes, group vice president of SCM product management at Oracle, emphasized the significance of these advancements, stating, “Process manufacturers in complex, regulated industries must deliver consistent quality at scale despite high variability in materials, yields, and production conditions.” Among the latest capabilities, a key focus is on enhanced recipe and yield management. Oracle’s features allow for automatic synchronization of formula changes with recipes, which not only maintains product quality but also helps in adhering to regulatory requirements. Manufacturers can use AI-assisted “what-if” scenarios to analyze how changes may impact batch outcomes, thus making informed adjustments to their processes. Additionally, the platform introduces flexible batch manufacturing execution. This allows manufacturers to define batch size ranges that correspond to fluctuating demand, ensuring they can meet customer needs without overproducing. The system dynamically tracks actual materials and outputs, making it easier for businesses to maintain accurate production records. Another standout feature is the connected process execution, which fosters improved traceability and reduces errors. By enabling manufacturers to issue materials multiple times within a single operation and manage co-products efficiently, the platform aims to streamline production processes. Notably, Oracle has also implemented advanced materials traceability and control capabilities. Features such as lot-specific unit of measure conversions enhance inventory management and help keep track of lot quality. The system automatically prevents the use of expired materials, minimizing waste and risks associated with subpar products. For small business owners, these innovations could lead to optimized operations and enhanced product quality, potentially boosting customer satisfaction and retention. However, there are some challenges to consider. Transitioning to a cloud-based solution involves an initial investment in technology and training. Additionally, ensuring that all staff are adept at using the new tools might require ongoing training and development. Still, small businesses can leverage Oracle’s capabilities to transform their operational landscape. The consolidation of supply chain processes into a single AI-powered platform can lead to improved resilience and adaptability as market conditions evolve. Embedded AI features can analyze data and automate workflows, providing manufacturers with actionable insights to navigate supply challenges. As Oracle continues to enhance its offerings, small businesses in the manufacturing sector have the opportunity to benefit from advanced technologies that promote efficiency and compliance. The evolving landscape of process manufacturing, when coupled with these innovations, can ultimately help small manufacturers remain competitive in a challenging market. Learn more about Oracle’s latest innovations in supply chain and manufacturing at Oracle SCM. For the original press release detailing these advancements, visit Oracle News Release. Image via Google Gemini This article, "Oracle Unveils Enhancements for Real-Time Production in Process Manufacturing" was first published on Small Business Trends View the full article

Digital marketing teams have long debated the balance between SEO and PPC. Who owns the keyword? Who gets the budget? Who proves ROI most effectively? For years, the division felt clear. SEO optimized for organic rankings, while paid media optimized for auctions. Both fought for visibility on the same results page, but operated under fundamentally different mechanics and incentives. ChatGPT ads are beginning to erase that line. The separation between organic and paid isn’t just blurring, it’s breaking down inside conversational AI. The new battleground isn’t the SERP. It’s the prompt. The intersection of PPC and SEO now lives inside ChatGPT ads. From SERP-based strategy to prompt-based demand insights Search marketing has always revolved around keywords: bidding strategies, landing page optimization, and even attribution modeling. Generative AI doesn’t operate on keyword strings the same way. It operates on intent-rich, multi-variable prompts. “Best CRM” becomes “What’s the best CRM for a B2B SaaS company under 50 employees?” “Project management tool” becomes “What project management tool integrates with Slack and Notion?” These prompts carry deeper layers of context and specificity that traditional keyword research often flattens to accommodate SERP coverage rather than answer an individualized question. When ChatGPT introduces sponsored placements beneath its answers, ads don’t appear next to a head term. They show under a fully articulated need. That changes everything. ChatGPT ads are structurally different. They: Appear underneath an AI-generated response. Are clearly labeled as “Sponsored.” Don’t influence the answer itself. Are primarily contextual and session-based. This isn’t a classic auction layered over a keyword strategy. It’s contextual alignment layered over a conversational experience. For marketers, that means three things: Intent is richer. Context matters more. SEO and PPC must coordinate at the prompt level. Dig deeper: Ads in ChatGPT: Why behavior matters more than targeting 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 new playbook: Prompt intelligence as the bridge If ChatGPT ads represent a new demand capture environment, the first strategic question becomes, “How do we know which prompts to prioritize?” The answer isn’t buried in Google Search Console, Keyword Planner, or any other SERP research or keyword mining tool. It’s surfaced in LLM performance that SEO counterparts have been analyzing for the past several months. The first intersection of PPC and SEO begins with organic LLM visibility. We can start developing a ChatGPT ads strategy by mining high-performing LLM prompts. To do this, we’ll need to understand: When does your brand appear organically in ChatGPT responses, and when do competitors appear? What types of prompts surface the kinds of discussions we want to be part of? Which use cases are most commonly referenced? This is prompt intelligence. Instead of asking, “What keywords are we ranking for?” the question becomes, “Which conversational queries are surfacing our brand?” When you analyze those prompts, you uncover something even more valuable: fanout keywords. Fanout keywords: The new long tail Fanout keywords are contextual signals embedded within prompts. For example, take this prompt: “Best CRM for B2B SaaS startups with under 50 employees that integrates with HubSpot.” Traditional keyword tools might surface relevant targets as “CRM for SaaS,” “best CRM,” and “B2B CRM,” focusing on the root terms and the core subject of the prompt. The fanout structure would include “SaaS startups with under 50 employees,” “HubSpot integration,” “budget sensitivity,” and “growth-stage scaling,” focusing not only on the root terms and core subject but also on factors like company size, growth trajectory, and pain-point considerations. These aren’t simple keyword variations to cover semantic phrasing. They’re layered qualifiers that reveal nuance and support us as marketers in identifying additional high-intent segments, highlighting underserved or undiscovered audience segments, and identifying potential gaps in paid keyword coverage. This is an example of PPC and SEO converging. Dig deeper: Why AI optimization is just long-tail SEO done right Aligning fanout keywords with paid coverage After extracting fanout keywords from high-performing LLM prompts, run a paid coverage audit to see whether your strategy addresses the nuanced variants that surfaced, whether you’re over-indexed on root terms while missing higher-intent expansions, and whether competitors dominate contextual areas you’ve overlooked. You can prioritize where to activate paid media based on this audit: If LLM organic presence is high and paid media coverage is high: Great. Continue reinforcing your strategy to dominate. If LLM organic presence is high and paid media coverage is low: Consider testing ChatGPT ads to increase overall coverage. If LLM organic presence is low and paid media coverage is high: Work on improving organic LLM and SEO visibility and strength. If LLM organic presence is low and paid media coverage is low: This is a lower priority. Focus on building foundational marketing strategies to increase overall coverage. The opportunity lies where organic LLM visibility and paid gaps intersect. If your brand frequently appears in conversational responses for “CRM for early-stage SaaS,” but you aren’t targeting that intent via paid placements, you’re leaving incremental demand on the table. ChatGPT ads can become a mechanism for defending and amplifying organic AI authority. Landing pages: An overlooked leverage point Until now, PPC and SEO teams may have both sent traffic to the same landing pages, but each team optimized them based on independent factors. That approach won’t hold in conversational AI. When prompts become hyper-specific, landing pages must mirror that specificity. Consider this group of queries: “Best CRM for 10-person SaaS team,” “Affordable CRM for startups,” and “CRM with simple onboarding for founders.” If all of those drive to a generic “CRM software” page, conversion friction increases and conversion rates drop. Instead, we can use these groups to build intent-specific landing pages, add content tied to common keyword fanout themes, adjust messaging to mirror conversational phrasing, and highlight deeper, relevant information for the customer. The more your landing page reflects the nuance of the prompt, the stronger alignment becomes across ad relevance, user experience, conversion performance, and even LLM organic authority. The critical loop is this: Improved landing page clarity doesn’t just increase conversion. It increases the likelihood that LLMs understand and surface your brand appropriately in future prompts. This is the new feedback cycle between SEO and paid. Get the newsletter search marketers rely on. See terms. The closed loop between LLM visibility and paid media In traditional search, SEO influenced PPC through factors like Quality Score and brand demand. Paid media influenced SEO indirectly through brand lift. With conversational AI, the loop tightens. Organic LLM visibility surfaces prompt clusters. Prompt clusters inform ChatGPT ad prioritization. Paid performance identifies high-converting conversational segments. Landing page optimizations improve both conversion and LLM clarity. Improved clarity increases organic AI mentions. This isn’t parallel channel management anymore. It has to be a unified system. Dig deeper: SEO vs. PPC vs. AI: The visibility dilemma Measurement: Moving beyond last click One of the most common objections to emerging ad formats is the ability to accurately measure performance and report ROI. ChatGPT ads operate with privacy-forward controls and aggregate reporting. We won’t have pixel-level behavioral depth or cross-session tracking parity with traditional paid media. This continues to force a shift in how marketing performance is evaluated, away from click-based attribution models. Instead of relying exclusively on click-based ROI, teams should prioritize: Incrementality testing. Assisted conversion analysis. Prompt-level lift. Brand search lift post-exposure. LLM visibility shifts before and after paid media campaign coverage. If ChatGPT ads reinforce high-intent conversational exposure, that impact might show up downstream in branded search, direct traffic, and higher close rates in assisted funnels. We shouldn’t think of this as a purely demand capture channel, but as a hybrid of capture and demand influence or creation. Organizational implications: SEO and PPC can’t be siloed This shift is less about media buying and more about team structure. To execute effectively, marketing organizations need to prioritize. 1. Shared prompt taxonomies SEO and paid teams must work together to group queries into prompt categories. For example, role-based queries (e.g., CMO, founder, or operations lead); industry-based queries (e.g., SaaS, healthcare, or ecommerce); and constraint-based queries (e.g., budget, team size, or integrations). These groupings should inform both content and paid media structure and bidding strategies. 2. Unified reporting dashboards Instead of separate keyword and ranking reports, teams should see: Query group performance. LLM visibility by segment. Paid coverage by segment or query group. Landing page conversion by prompt type or category. 3. Integrated budget planning Paid media budget allocation should consider where: Organic AI authority is strongest. Competitors dominate conversational mentions. Incremental coverage via ChatGPT ads can defend or expand. This isn’t about shifting dollars from Google Ads to ChatGPT. It’s about reallocating dollars based on a deeper understanding of user demand and behavior. Dig deeper: Why 2026 is the year the SEO silo breaks and cross-channel execution starts The bigger shift: AI as the primary discovery layer Zoom out. Search engines were the gateway to information. Social feeds were the gateway to discovery. Conversational AI is becoming the gateway to decision-making. If that trajectory continues, optimizing for LLM visibility becomes as critical as ranking on Google once was. Now that ads are layered into that experience, paid media and SEO become inseparable. The future won’t be defined by organic rankings or paid media CPC efficiency alone. It will be defined by how effectively brands show a unified message and experience across: Prompt intelligence. Contextual ad placement. Landing page alignment. Conversational authority. 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 Think in systems, not channels The introduction of ads into ChatGPT isn’t just another platform beta. It’s a structural signal. The channel divide between SEO and paid media, a debate that has shaped marketing teams for as long as they’ve existed, is dissolving inside conversational AI. The brands that win will: Mine prompt data like they once mined keyword reports. Extract fanout signals that reveal hidden demand. Align paid media coverage to conversational intent. Build landing pages that mirror prompt nuance. Measure incrementally and holistically, not myopically. The intersection of paid and SEO is no longer a shared SERP. It’s a shared intelligence system. ChatGPT ads may be the first clear signal that conversational AI isn’t just changing how people search. It’s changing how we structure growth. View the full article

The social media trial brought by a 20-year-old Californian plaintiff known as Kaley or KGM, putting Meta and YouTube in front of a jury, has captured the world’s attention. The bellwether trial is a test case for the liability of social media platforms and how much they could be on the hook financially if found to have caused harm to their users. KGM, for her part, alleges that she faced anxiety, depression and body image issues after using Instagram. The proceedings could establish the first real legal boundaries for what has been up to now largely unregulated algorithmic design, determining whether amplifying harmful content amounts to negligence. A verdict against Meta or YouTube in this bellwether case could open the door to other suits, and finally force disclosure of internal research that has so far remained confidential. The first day that Mark Zuckerberg, Meta’s CEO, was on the stand on February 18 was a major moment—not necessarily for what Zuckerberg said, but for the fact the case has gotten this far. “This is a significant moment in terms of these platforms finally being seen to be held to account by their own users,” says Steven Buckley, lecturer in media digital and sociology at City St George’s, University of London. While Zuckerberg withstood rigorous questioning from Mark Lanier, the lawyer representing Kaley GM, the fact that he was there at all and the case got to trial is a significant happening. As Fast Company has previously reported, 2026 is the year that the world is getting tough on online safety, particularly for kids. And this trial is notable because it managed to sidestep the usual way social networks swerve liability: Claiming Section 230 protections, which have been in place since the mid-1990s and insulate platforms from bearing responsibility for the actions of their users. If jurors agree that product design, rather than user behavior, is the root cause of harm, big tech’s decades-long legal shield could begin to fracture. That possibility alone has Silicon Valley watching nervously, with billions in potential damages on the line. Prior to the trial beginning, Snap and TikTok settled with the claimant without admission of liability, leaving YouTube and Meta to fight the trial. A Meta spokesperson tells Fast Company the firm “strongly disagree with these allegations and are confident the evidence will show our longstanding commitment to supporting young people.” They add: “The evidence will show she faced many significant, difficult challenges well before she ever used social media.”(YouTube responded to Fast Company’s request for comment.) “It’s not particularly surprising that these large platforms are finally facing some legal repercussions from their actual users,” says Buckley. A steady drumbeat of reporting, alongside other smaller legal cases, have revealed information that suggests social media can be harmful to younger users. This case is therefore a potential watershed because the plaintiffs argue that Instagram’s and YouTube’s underlying product design—features like the infinite scroll, autoplay and recommendation algorithms that serve up progressively more engaging content—constitutes a defective product. But most of those other cases haven’t received as much attention because they’ve not gotten as far as this one has—nor have been as likely to succeed in some way. “Zuckerberg did not come across as someone with children’s best interests at heart,” says Tama Leaver, professor of internet studies at Curtin University in Australia. Leaver contrasts Zuckerberg’s performance in court with Adam Mosseri a few days earlier, who the researcher says “had the tenacity to argue that the term addiction is being misused”. In contrast, “Zuckerberg didn’t feel like someone who’d done their homework, but rather someone who was surprised they had to turn up and answer these questions,” Leaver explains. “If his job was to convince the listening world that he could be a trusted figure in the lives of teens and young people, then he failed.” Despite that poor performance by Zuckerberg, and despite the strength of the case in comparison to others that have gone before, some think that a decision against the social media firms—or a general movement to recognize the issues inherent with social media—could backfire. “One concern I have is that people will think that the simple solution to many of the issues raised in these lawsuits is to simply ban under-16s from using the platforms,” says Buckley. “This is a woefully misguided reaction. The scientific evidence regarding the link between social media use at a young age and addiction is still not well established.” Whether the jury agrees with that assessment or not, the trial has already achieved something that years of congressional hearings and regulatory hand-wringing haven’t: Putting the people who designed these systems under oath and making them answer difficult questions—then be responsible for the consequences of what they say. “One of the reasons I think we have gotten to this stage is that some people have come to the conclusion that their governments are not going to do anything meaningful to hold these companies to account and so have felt compelled to take them on themselves,” says Buckley. The rest of the tech industry will be watching closely to see what comes next. View the full article

Tariffs paid by midsized U.S. businesses tripled over the course of last year, new research tied to one of America’s leading banks showed on Thursday — more evidence that President Donald The President’s push to charge higher taxes on imports is causing economic disruption. The additional taxes have meant that companies that employ a combined 48 million people in the U.S. — the kinds of businesses that The President had promised to revive — have had to find ways to absorb the new expense, by passing it along to customers in the form of higher prices, employing fewer workers or accepting lower profits. “That’s a big change in their cost of doing business,” said Chi Mac, business research director of the JPMorganChase Institute, which published the analysis on Thursday. “We also see some indications that they may be shifting away from transacting with China and maybe toward some other regions in Asia.” The research doesn’t say how the additional costs are flowing through the economy, but it indicates that tariffs are being paid by U.S. firms. It’s part of a growing body of economic analyses that counter the administration’s claims that foreigners pay the tariffs. The JPMorganChase Institute report used payments data to look at businesses that might lack the pricing power of large multinational companies to offset tariffs, but may be small enough to quickly change supply chains to minimize exposure to the tax increases. The companies tended to have revenues between $10 million and $1 billion with fewer than 500 employees, a category known as “middle market.” The analysis suggests that the The President administration’s goal of becoming less directly reliant on Chinese manufacturers has been occurring. Payments to China by these companies were 20% below their October 2024 levels, but it’s unclear whether that means China is simply routing its goods through other countries or if supply chains have moved. The authors of the analysis emphasized in an interview that companies are still adjusting to the tariffs and said they plan to continue studying the issue. The The President administration has been adamant that the tariffs are a boon for the economy, businesses, and workers. Kevin Hassett, director of the White House National Economic Council, lashed out on Wednesday at research by the New York Federal Reserve showing that nearly 90% of the burden for The President’s tariffs fell on U.S. companies and consumers. “The paper is an embarrassment,” Hassett told CNBC. “It’s, I think, the worst paper I’ve ever seen in the history of the Federal Reserve system. The people associated with this paper should presumably be disciplined.” The President increased the average tariff rate to 13% from 2.6% last year, according to the New York Fed researchers. He declared that tariffs on some items like steel, kitchen cabinets and bathroom vanities were in the national security interest of the country — and declared an economic emergency to bypass Congress and impose a baseline tax on goods from much of the world last April at an event he called “Liberation Day.” The high rates provoked a financial market panic, prompting The President to walk back his rates and then engage in talks with multiple countries that led to a set of new trade frameworks. The Supreme Court is expected to rule soon on whether The President surpassed his legal authority by declaring an economic emergency. The President was elected in 2024 on his promise to tame inflation, but his tariffs have contributed to voter frustration over affordability. While inflation has not spiked during The President’s term thus far, hiring slowed sharply and a team of academic economists estimate that consumer prices were roughly 0.8 percentage points higher than they would otherwise be. —Josh Boak, Associated Press View the full article

Long-form content doesn’t fail because it’s weak. It fails because LLMs lose the middle. This article explains how to engineer it to survive. The post Why AI Misreads The Middle Of Your Best Pages appeared first on Search Engine Journal. View the full article

You don't need to do much digging online to find complaints about the iPhone keyboard: From typos and spelling mistakes to lag and missed keystrokes, there are multiple issues being reported by users, across multiple versions of iOS. While the root causes of these problems tend to vary, there are some broad fixes you can try that should go some way to giving you an iPhone keyboard experience you can rely on—besides waiting for the next bug-squashing iOS update from Apple. Reset the keyboard dictionary on your iPhone Resetting the keyboard on iOS. Credit: Lifehacker Over time, the iOS keyboard tries to build up smarter autocorrect suggestions for you, but these aren't always helpful: The further away these suggestions get from the defaults, the worse they can get, which risks turning your sentences into gibberish. To clean the slate and go back to the beginning, open up Settings, then tap General > Transfer or Reset iPhone > Reset > Reset Keyboard Dictionary. Type in your handset's unlock code, then choose Reset Dictionary. Add custom words and shortcuts to your iPhone's dictionaryThis may seem to contradict the previous tip, but by explicitly teaching your iPhone the words it often gets wrong, you can reduce the likelihood of those frustrating moments where iOS suddenly replaces the word you were typing with something else (e.g. "he'll yeah" or "what the duck"). From Settings, tap General > Keyboard > Text Replacement. Tap on the + (plus) button in the top right corner, then enter your word or phrase—you can add a shortcut for it too to help you type it more quickly, but it's optional. Tap Save to confirm. Adjust other iPhone keyboard settings If a keyboard setting isn't helping, turn it off. Credit: Lifehacker There are more keyboard settings that are worth taking a look at under General > Keyboard in iOS Settings. You can turn Auto-Correction off completely, for example, and disable Slide to Type if your fingers have a tendency to slip across the keyboard. There are additional tweaks you can make through the Accessibility menu in Settings: Under Touch > Touch Accommodations, you can change the sensitivity of double-taps and press-and-holds, among other settings, which may help improve typing accuracy. Change your iPhone's keyboard layoutThere's not a lot you can do with the iPhone keyboard layout to improve your typing experience and eliminate bugs, but there is a one-handed mode you can try in order to minimize glitches and ensure your keypresses match up with what's on screen. To switch to the one-handed layout, tap and hold on the globe icon in the lower left corner of the keyboard, then tap one of the icons at the bottom of the pop-up menu: You can move the keyboard to the left, or the right, or put it back to normal. Switch to a different keyboard on the iPhone You've got several options for iOS keyboards. Credit: Lifehacker If you're still struggling with the vagaries of the keyboard on iOS, you can always opt to install a third-party alternative: We've covered a bunch here, including Gboard and SwiftKey, and they typically offer more customization options than the Apple default. Once you've installed an alternative keyboard or two, you can manage them from iOS Settings by choosing General > Keyboard > Keyboards. To actually switch between keyboards when typing, press and hold on the globe icon (lower left). Or avoid a keyboard altogetherThere are also some more extreme measures you can take that maybe haven't crossed your mind. The first is to use a Bluetooth keyboard (via Bluetooth in Settings), which will give you a more convenient (if less portable) way of typing text into your iPhone. The second is to ensure dictation is enabled in General > Keyboard in Settings, then tap the mic icon in the lower right corner of the keyboard, and speak out your text. You can handle emojis, line breaks, text editing, and more, using your voice. View the full article

A phrase from the 1990s altered America’s sense of itself — and its political trajectoryView the full article

Collapse follows revelations about former peer’s relationship with Jeffrey EpsteinView the full article

As AI automates compliance, value shifts to measurable outcomes and client aspirations. Gear Up for Growth With Jean Caragher For CPA Trendlines Go PRO for members-only access to more Jean Marie Caragher. View the full article

As AI automates compliance, value shifts to measurable outcomes and client aspirations. Gear Up for Growth With Jean Caragher For CPA Trendlines Go PRO for members-only access to more Jean Marie Caragher. View the full article

New data highlights spiralling import costs as president seeks to quell growing backlash over his flagship policiesView the full article

Dive into the discussions around SEO adaptations and LLM impact on the industry with host Shelley Walsh and industry veteran Grant Simmons. The post 35-Year SEO Veteran: Great SEO Is Good GEO — But Not Everyone’s Been Doing Great SEO appeared first on Search Engine Journal. 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 75-inch LG evo AI Mini LED 4K Smart TV (75QNED85AUA) is down to $896.99 from $1,396.99 on Amazon, which is the lowest price it has hit so far, according to price trackers. It lands in the middle of LG’s QNED range, sitting above the QNED82A and below the QNED92A. In practical terms, that means you’re getting a big 75-inch screen with Mini LED backlighting and local dimming, plus LG’s latest α8 AI Processor Gen2 handling the picture. LG 75-Inch Class QNED evo AI QNED85A Series Mini LED 4K Smart TV $896.99 at Amazon $1,396.99 Save $500.00 Get Deal Get Deal $896.99 at Amazon $1,396.99 Save $500.00 There are four HDMI ports, and all of them can do 4K at 120Hz, so it pairs well with a PlayStation 5 or Xbox Series X. It supports common HDR formats like HDR10 and HLG, and it has Wi-Fi 6E, Bluetooth 5.3, and eARC if you want to hook up a soundbar. LG’s 2025 version of webOS runs the interface, and the company says it will provide software updates for five years through its Re:New program. In day-to-day use, this TV does a lot right. Regular HD channels and cable broadcasts look solid once you tweak the basic settings. Colors in standard viewing modes look natural instead of overly saturated. Older or lower-resolution content, like DVDs or heavily compressed streams, looks cleaner than you might expect on a screen this large. Motion is handled well for most shows and sports. Slow camera pans in movies look smooth, without that distracting stutter. For gaming, having four HDMI ports that all support 4K at 120Hz is convenient, especially if you have more than one console. Plus, variable refresh rate (VRR) helps cut down on screen tearing during fast gameplay. Where it falls short is in contrast and brightness. Because it is edge-lit, the black levels are not especially deep, and you can sometimes see a faint glow around bright objects on dark backgrounds. HDR movies do not have the same pop you’d get from a brighter or more advanced panel. And fast, dark action scenes may show a bit of blur. It can also struggle in a very sunny room, where glare becomes noticeable. Still, at under $900 for 75 inches, this is a lot of screen for the money. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods 4 Active Noise Cancelling Wireless Earbuds — $139.99 (List Price $179.00) Apple iPad 11" 128GB A16 WiFi Tablet (Blue, 2025) — $329.00 (List Price $349.00) Google Pixel 10a 128GB 6.3" Unlocked Smartphone + $100 Gift Card — $499.00 (List Price $599.00) Apple Watch Series 11 [GPS 46mm] Smartwatch with Jet Black Aluminum Case with Black Sport Band - M/L. Sleep Score, Fitness Tracker, Health Monitoring, Always-On Display, Water Resistant — $329.00 (List Price $429.00) Amazon Fire TV Stick 4K Plus — $29.99 (List Price $49.99) Bose QuietComfort Noise Cancelling Wireless Headphones — $229.99 (List Price $349.00) Samsung Galaxy Tab A9+ 64GB Wi-Fi 11" Tablet (Silver) — $159.99 (List Price $219.99) Deals are selected by our commerce team View the full article

Can AI help neurodivergent adults connect with each other? That’s the bet of a new social network called Synchrony, which believes AI and a well-designed social network with the right safeguards can reduce social atomization and calm the overwhelming cacophony of socializing online. Launching February 19, the social network debuts during a moment when social media, chatbots, and doomscrolling has made digital communications a hot button topic for parents. “No other app for the neurodiverse is focusing primarily on reducing social anxiety and encouraging friendship,” says cofounder Jamie Pastrano. “I think that’s the biggest piece of it, and no other app is focusing on building an authentic community.” Synchrony also has support from Starry Foundation and Autism Speaks, two large U.S. advocacy groups, and approval from the Apple App Store. “I was really blown away about what they’re trying to do,” says Bobby Vossoughi, president of the Starry Foundation. “These kids are isolated and their social cues are off. They’re creating something that could really change this community’s lives for the long term.” A parenting challenge without a solution The idea for Synchrony came from Pastrano, a former management consultant and executive sales leader, whose son, Jesse, 21, is autistic. As Jesse experienced teenagerhood, Pastrano became frustrated with the challenges she saw her son facing around the friendship gap; she saw him as a social kid, but planning, timing, even saying the appropriate thing often tripped him up. Unlike other challenges she’d faced as a mother of a neurodivergent child, this one didn’t seem to have a solution. Research shows that people with autism or neuro developmental differences—roughly 1 in 5 people according to the Neurodiversity Alliance—face increasing loneliness as they transition between adolescence and adulthood. New social responsibilities and expectations for life after school, combined with the loss of support systems that may have been embedded in secondary and university education, can lead to isolation. One of the cofounders, Brittany Moser, an autism specialist who teaches at Park University in Missouri, says that she’s held crying students who, forced to operate in a world that’s not built for them, are desperate for social connection. She hopes this network can foster it. “Autism doesn’t end at 18,” Pastrano says. “There was this huge gap in services to support social, emotional, and community needs.” Pastrano sold her company in 2024 and devoted herself to solving the issue with what would become Synchrony. Part of Pastrano’s inspiration came from reality television. The dating show Love on the Spectrum piqued her interest, causing her to think not about romance, but about connection, friendship, and community. She even contacted a coach on the show, who suggested she get certified at the PEERS program at UCLA, which teaches social and dating skills to young adults on the spectrum. Broadly speaking, Synchrony is built with the same digital infrastructure as a dating site, but is meant for fostering friendships amid a unique population. A big part of the design challenge was making sure it was suitable for the audience, and wasn’t too distracting or loud. Profiles focus much more on interests, Pastrano says, since interests weigh much more heavily as a reason to communicate among this population. There’s also a space to list neurodiversity classifications and communication style and preferences (“I prefer text to phone calls,” or “I take a few days to reply,” etc.) as part of the effort to front-load key details. Simplified menus and colors and no ads help reduce distractions. Pastrano also wants to respect the community and focus on healthy experiences and not push for rapid growth; users pay a monthly fee of $44.99 after a free 30-day trial, allowing the network to avoid advertisements. Part of the registration process includes two-step verification—both the user and a trusted person, either a teacher, doctor, or parent needs to input personal details and a photo ID—to make sure bad actors outside the community aren’t given access. Social Coach, or ‘Seductive Cul-de-sac’ Part of Synchrony’s strategy is the use of Jesse (named after Pastrano’s son), marketed as an “AI-powered social support tool that goes far beyond chat assist technology.” By providing real-time conversation support, the chatbot aims to overcome social anxiety and a lack of confidence around socialization. Talking with Jesse online, developers claim, will bolster user self-assurance and communication skills, eventually manifesting in real life. When Synchrony users get stuck in an online conversation, they can tap an icon to summon Jesse, who will provide editable solutions to advance or end an interaction. The AI coach offers three main options: a tool to help express yourself, that will offer solutions to continuing the conversation; a button that can help parse through the conversation to help better understand what happened, and whether something might have been meant as flirty or friendly; and a final option to protect, and offer suggestions to set boundaries and exit a conversation quietly. Built using the Amazon Bedrock large language model and trained by Synchrony staff, Jesse is scanning conversations constantly to provide social coaching when asked. The use of AI among the neurodivergent population has sparked the same debates as the technology’s use among the population at large. Research by a team at Stanford found that an AI chatbot they developed called Noora, designed to improve communication skills, can improve empathy among users with autism. Some members of the community have claimed AI coaches have helped them with relationships and “transformed” their lives. At the same time, some advocacy groups have warned that chatbot’s emotional manipulation can be more severe for the neurodiverse, and some researchers are concerned AI might reinforce bad communication habits. British researcher Chris Papadopoulos sums up the state of play in a recent paper, concluding that while “the technology holds the potential to democratize companionship… left unchecked, AI companions could become a seductive cul-de-sac, capturing autistic people in artificial relationships that stunt their growth or even lead them into harm’s way.” Amid awareness of the sometimes destructive and even deadly consequences of chatbot use, there are significant guardrails built into Jesse, says Moser, including a long list of activities and actions to avoid, like not sharing personal addresses. Jesse is also told not to dispense medical advice. Jesse is not a therapist, and as the founders are clear to note, this isn’t a clinical app. If users start asking Jesse about off-topic concepts, Moser says it will be programmed to reply something to the effect of, “Hmm, I don’t know if that’s really going to help you connect with the other members.” There will also be warnings if someone is spending too much time just talking with Jesse. Synchrony is launching with human moderation to provide extra safeguards. Lynn Koegel, a professor and researcher at Stanford University who has studied autism and technology, says her team has spent time updating and changing their models of Noora, to make sure it’s not too harsh, such as not reinforcing communication attempts or being too strict around grammar issues. She says it’s very important to do more in-depth studies and clinical research to make sure these tools do work well and as intended (she has not seen or tested Synchrony). “My gut feeling is these tools can be very good support,” she says. “The jury is out about whether individual programs that haven’t been tested can be assistive.” As the Synchrony team works out bugs and final design issues before launch, the challenge becomes building a robust enough community to drive more organic growth. Early user testing that started in December, both an alpha test of 14 users, and closed beta tests among university support groups for autistic students, helped them refine the model and layout. The marketing strategy at launch doesn’t focus on the users themselves, but rather neurodiverse employer groups, universities that have neurodiverse programs (who can create their own closed-loop, campus versions of the app), advocates, and relevant podcast hosts. “Success is about awareness and attention,” says Pastrano. “It’s not a numbers game for me. It’s a really personal game.” View the full article

Google is launching Scenario Planner, a no-code tool that lets you test budget scenarios and forecast ROI using its Meridian marketing mix model without needing data science expertise. What’s new. Scenario Planner turns complex MMM outputs into actionable marketing insights: Intuitive, code-free interface: You can test different budget allocations and view ROI estimates without writing any code. Forward-looking planning: The tool lets you simulate investment scenarios and stress-test strategies, moving beyond retrospective reporting. Digestible insights: Technical model outputs are visualized in clear, easy-to-understand formats so you can leverage them for strategy decisions. Why we care. With predictive marketing insights at your fingertips, you can test budgets, predict returns, and adjust campaigns in real time — so you plan smarter and make the most of every dollar. Closing the MMM actionability gap. Scenario Planner bridges the long-standing “usability gap” in Marketing Mix Models, which traditionally required specialized skills. Nearly 40% of organizations struggle to turn MMM outputs into actionable decisions, according to Harvard Business Review. Bottom line. By combining the rigor of MMM with an intuitive, interactive interface, Scenario Planner helps you plan smarter, optimize your spend, and make confident, data-driven decisions — without relying on technical experts. View the full article

Military build-up and fraught rhetoric leave a narrow path to securing a deal that would allow both sides to save faceView the full article

If the thought of AI smart glasses annoys you, you’re not alone. This week, the judge presiding over a historic social media addiction trial took a harsh stance on the AI-powered gadgets, which many bystanders find invasive of their privacy: Stop recording or face contempt of court. Here’s what you need to know. What’s happened? Yesterday, Meta CEO Mark Zuckerberg took the stand in a trial that many industry watchers say could have severe ramifications for social media giants, depending on how it turns out. At the heart of the trial is the question of whether social media companies like Meta, via its Facebook and Instagram platforms, purposely designed said platforms to be addictive. Since the trial began, many Big Tech execs have taken the stand to give testimony, and yesterday it was Meta CEO Mark Zuckerberg’s turn. But while Zuckerberg was there to talk about his legacy products—Facebook and Instagram, particularly—for a brief moment, the presiding judge in the case, Judge Carolyn B. Kuhl, turned her attention to a newer Meta product: the company’s Ray-Ban Meta AI Glasses. Judge warns AI smart glasses wearers According to multiple reports, at one point during yesterday’s trial, Judge Carolyn B. Kuhl took a moment to issue a stark warning to anyone wearing AI glasses in the courtroom: stop recording with them and delete the footage, or face contempt. Many courts generally forbid recording during trials, though there are exceptions. However, while the judge did seem to be worried about recording in general, she also had another concern: the privacy of the jury. “If your glasses are recording, you must take them off,” the judge said, per the Los Angeles Times. “It is the order of this court that there must be no facial recognition of the jury. If you have done that, you must delete it. This is very serious.” Currently, Meta’s AI glasses do not include the ability to identify the names of the people a wearer views through them, but that’s not likely what the judge meant in her concerns about “facial recognition.” Instead, it is likely the judge was concerned that the video recorded by the AI glasses could then be later viewed and run through external facial recognition software to identify the jury in the video. Some of Meta’s AI glasses can record video clips up to three minutes long. From reports, it does not appear as if the judge singled out any specific individuals in the courtroom, but CNBC reports that ahead of Mark Zuckerberg’s testimony, members of his team, escorting him into the building, were spotted wearing Meta Ray-Ban artificial intelligence glasses. As the LA Times reported, the judge’s “admonition was met with silence in the courtroom.” Broader social concerns over AI glasses The privacy of jurors is critical for fair and impartial trials, as well as their own safety. Given that, it’s no surprise that the judge did not mince words when warning about AI glasses recording. But the judge’s courtroom concerns also mirror many people’s broader concerns over AI glasses: People are worried about wearers of the glasses violating their privacy, either by recording them or using facial recognition to identify them. This concern first became evident more than a decade ago after Google introduced its now-failed smart glasses called Google Glass. Wearers of the device soon became known as “glassholes” due to what many bystanders felt was their intrusive nature. When talking to a person wearing smart glasses, you can never be sure you aren’t being recorded—and that freaks people out. That apprehension about smart glasses has not gone away in the years since Google Glass’s demise. Modern smart glasses are much more capable and concealed. At the same time, everyday consumers are more concerned about their privacy than ever. These privacy concerns will continue to be a major hurdle to AI smart glasses adoption—especially as AI smart glasses manufacturers, including Meta, reportedly plan to add facial recognition features in the future. The judge’s admonishment of AI glasses wearers in the courtroom yesterday won’t help the devices’ already strained reputation. View the full article

To effectively boost customer loyalty, it’s vital to adopt best practices that improve retention rates. Start by prioritizing exceptional customer service, which sets a solid foundation for positive experiences. Next, actively seek and value customer feedback, allowing you to comprehend their needs better. Personalizing interactions can make customers feel valued, whereas implementing loyalty programs rewards their continued support. Finally, nurturing a culture of continuous improvement guarantees your strategies remain relevant. Recognizing these fundamentals is key to long-term success. Key Takeaways Prioritize exceptional customer service to enhance satisfaction and retention, as 70% of consumers make purchases based on service quality. Actively seek and value customer feedback to foster a sense of community and improve loyalty through systematic analysis. Personalize customer interactions by offering tailored recommendations and understanding generational preferences to boost engagement and satisfaction. Implement effective loyalty programs with immediate rewards and exclusive perks to encourage repeat purchases and brand loyalty. Foster a culture of continuous improvement by using customer insights to refine strategies and ensure consistent service across all channels. Prioritize Exceptional Customer Service In today’s competitive market, prioritizing exceptional customer service is vital for retaining customers and nurturing loyalty. Studies show that 70% of consumers have made purchases based on the quality of service they received. By focusing on customer service, businesses can achieve a 90% increase in customer retention, emphasizing its direct correlation with loyalty. Empowering and training your staff improves their ability to provide knowledgeable service, as 63% of customers prefer returning to businesses that exhibit expertise. Furthermore, timely responses to inquiries boost satisfaction rates by 87%, highlighting the need for quick resolutions. In the end, implementing these customer retention best practices can greatly increase customer lifetime value, with loyal customers generating ten times more revenue than one-time buyers, reinforcing the importance of exceptional service. Actively Seek and Value Customer Feedback Exceptional customer service creates a strong foundation for customer loyalty, but actively seeking and valuing feedback can take that loyalty to the next level. To effectively learn how to retain customers, implement regular feedback collection through surveys and direct communication. Research shows that 96% of professionals prioritize gathering customer insights to identify improvement areas. By systematically analyzing this feedback, you can adapt your offerings, leading to higher satisfaction and loyalty. When customers see their opinions valued, they feel appreciated and are more likely to stay engaged with your brand. Furthermore, involving customers in the feedback process cultivates a sense of community, which improves loyalty and encourages positive word-of-mouth referrals, ultimately increasing your retention rates and strengthening your brand. Personalize Customer Interactions When businesses prioritize personalization in customer interactions, they greatly improve their chances of retaining customers. With 71% of consumers expecting customized experiences, it’s vital to integrate customer retention marketing strategies that focus on individual preferences. For instance, sending personalized recommendations based on past purchases can greatly improve customer satisfaction. Nonetheless, less than 50% of businesses currently provide such customized suggestions in their loyalty programs, revealing a considerable opportunity for improvement. Furthermore, comprehending generational communication preferences allows you to engage effectively; younger customers may prefer digital interactions, whereas older customers may favor in-store experiences. Implement Loyalty Programs Implementing effective loyalty programs can greatly improve customer retention and drive repeat purchases for your business. To improve your program, consider these strategies on how to increase customer retention: Offer Immediate Rewards: Consumers love discounts and points. Providing instant benefits encourages repeat visits and purchases. Create Tiered Programs: By implementing levels of rewards, you incentivize higher spending and cultivate brand loyalty through exclusive perks and recognition. Personalize Marketing Efforts: Use customer data to offer customized recommendations, which can markedly improve satisfaction and engagement. Foster a Culture of Continuous Improvement Nurturing a culture of continuous improvement is vital for businesses aiming to boost customer retention and satisfaction. By regularly gathering customer feedback, you can identify areas for refinement that directly impact loyalty. For instance, 96% of Voice of Customer professionals use surveys to analyze customer input. Implementing changes based on this feedback, like hardware stores adjusting inventory according to contractor needs, can lead to increased satisfaction. Additionally, since 87% of customers seek service consistency across channels, engaging in a continuous improvement cycle helps you adapt swiftly to their expectations. By leveraging insights from unstructured feedback, you can refine your customer service retention strategies, ensuring long-term growth and higher retention rates through consistently meeting customer needs. Frequently Asked Questions How Can Small Businesses Implement Customer Retention Strategies Effectively? To implement customer retention strategies effectively, you should focus on comprehending your customers’ needs. Start by collecting feedback through surveys or direct communication to identify areas for improvement. Next, personalize your services or products based on this feedback to improve customer experience. Furthermore, create loyalty programs that reward repeat purchases, and guarantee your customer service is responsive and helpful. Regularly engaging with customers through newsletters or social media can likewise strengthen relationships. What Role Does Social Media Play in Customer Retention? Social media plays an essential role in customer retention by facilitating direct communication between you and your customers. It allows you to share updates, respond to inquiries, and gather feedback quickly. For instance, you can use platforms like Facebook or Instagram to post engaging content that keeps customers informed about promotions or new products. Furthermore, showcasing customer testimonials on these platforms can improve trust and encourage loyalty, making customers feel valued and connected to your brand. How Can Data Analytics Improve Customer Retention Efforts? Data analytics can greatly improve your customer retention efforts by identifying trends and behaviors. By analyzing purchase history, you can tailor marketing strategies to meet customer preferences. For instance, segmenting customers based on their buying patterns allows you to create personalized offers. Furthermore, tracking customer feedback through surveys helps pinpoint areas for improvement. Implementing these insights guarantees you address customer needs effectively, leading to increased satisfaction and loyalty over time. What Are Common Mistakes to Avoid in Customer Retention Strategies? When developing customer retention strategies, avoid common mistakes like neglecting feedback, failing to personalize communication, and ignoring data analytics. If you don’t listen to customer concerns, you miss opportunities for improvement. Personalization helps customers feel valued, so generic messages can alienate them. Furthermore, not leveraging data analytics means you might overlook trends and behaviors that indicate when a customer is likely to disengage. Prioritizing these aspects can improve your retention efforts greatly. How Often Should We Review Our Customer Retention Practices? You should review your customer retention practices at least quarterly. This frequency allows you to assess the effectiveness of your strategies and make timely adjustments. For instance, if you notice a drop in engagement, you can quickly analyze customer feedback and adapt your approach. Furthermore, annual reviews can help you evaluate long-term trends. Regular assessments guarantee you stay aligned with customer needs and market changes, ultimately improving retention and loyalty. Conclusion Incorporating these five best practices can greatly improve your customer retention efforts. By prioritizing exceptional service, valuing feedback, personalizing interactions, implementing effective loyalty programs, and promoting continuous improvement, you create a more engaging experience for your customers. These strategies not merely strengthen loyalty but also encourage long-term relationships. When customers feel valued and understood, they are more likely to return and recommend your business to others, finally driving sustained growth and success. Image via Google Gemini and ArtSmart This article, "5 Essential Best Practices for Customer Retention to Boost Loyalty" was first published on Small Business Trends View the full article