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You don’t need all the answers to be a leader—but you do need this mindset. Emma Grede explains why excellence is non-negotiable and why trying to please everyone will hold you back. This is the leadership advice nobody tells you. View the full article

Google is rolling out new Google Maps features that make it easier to contribute photos, reviews, and local insights, while adding Gemini-powered caption suggestions. Local Guides redesign. Contributor profiles are getting more visibility. Total points now appear more prominently, Local Guide levels are easier to spot, and badge designs have been refreshed. Top contributors will also stand out more in reviews with new gold profile indicators. AI caption drafts. Google is also introducing AI-generated caption drafts. Gemini analyzes selected images and suggests text you can edit or discard. Caption suggestions are available in English on iOS in the U.S., with Android and broader global expansion planned. Media sharing. Google Maps now shows recent photos and videos directly in the Contribute tab, making uploads faster. If you enable media access, Google Maps will suggest images from your camera roll that are ready to post with a tap. This feature is now live globally on iOS and Android. Why we care. Google is making it easier to create and scale fresh local content, which can directly affect rankings and visibility. At the same time, stronger contributor signals may influence which reviews users trust and which businesses win clicks. View the full article

Fights over regulation could reshape the US federal system of governmentView the full article

Mike Kortas will be adding a separate mortgage servicing company and hiring NEXA loan officers to assist with the process and give them customer insights. View the full article

San Francisco's median house price jumped to a record $2.15 million in March, up 18% from a year earlier as wealth generated by artificial intelligence startups flooded the city, according to brokerage Compass Inc. View the full article

Google once attributed two of Barry Schwartz’s Search Engine Land articles to me — a misclassification at the annotation layer that briefly rewrote authorship in Google’s systems. For a few days, when you searched for certain Search Engine Land articles Schwartz had written, Google listed me as the author. The articles appeared in my entity’s publication list and were connected to my Knowledge Panel. What happened illustrates something the SEO industry has almost entirely overlooked: that annotation — not the content itself — is the key to what users see and thus your success. How Google annotated the page and got the author wrong Googlebot crawled those pages, found my name prominently displayed below the article (my author bio appeared as the first recognized entity name beneath the content), and the algorithm at the annotation gate added the “Post-It” that classified me as the author with high confidence. This is the most important point to bear in mind: the bot can misclassify and annotate, and that defines everything the algorithms do downstream (in recruitment, grounding, display, and won). In this case, the issue was authorship, which isn’t going to kill my business or Schwartz’s. But if that were a product, a price, an attribute, or anything else that matters to the intent of a user search query where your brand should be one of the obvious candidates, when any aspect of content is inaccurately annotated, you’ve lost the “ranking game” before you even started competing. Annotation is the single most important gate in taking your brand from discover to won, whatever query, intent, or engine you’re optimizing for. 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 What annotation is and why it isn’t indexing Indexing (Gate 4) breaks your content into semantic chunks, converts it, and stores it in a proprietary format. Annotation (Gate 5) then labels those chunks with a confidence-driven “Post-It” classification system. It’s a pragmatic labeler and attaches classifications to each chunk, describing: What that chunk contains factually. In what circumstances it might be useful. The trustworthiness of the information. Importantly, it’s mostly unopinionated when labeling facts, context, and trustworthiness. Microsoft’s Fabrice Canel confirmed the principle that the bot tags without judging, and that filtering happens at query time. What does that mean? The bot annotates neutrally at crawl time, classifying your content without knowing what query will eventually trigger retrieval. Annotation carries no intent at all. It’s the insight that has completely changed my approach to “crawl and index.” That clearly shows you that indexing isn’t the ultimate goal. Getting your page indexed is table stakes. Full, correct, and confident annotation is where the action happens: an indexed page that is poorly annotated is invisible to each of the algorithmic trinity. The annotation system analyzes each chunk using one or more language models, cross-referenced against the web index, the knowledge graph, and the models’ own parametric knowledge. But it analyzes each chunk in the context of the page wrapper. The page-level topic, entity associations, and intent provide the frame for classifying each chunk. If the page-level understanding is confused (unclear topic, ambiguous entity, mixed intent), every chunk annotation inherits that confusion. Even more importantly, it assigns confidence to every piece of information it adds to the “Post-Its.” The choices happen downstream: each of the algorithmic trinity (LLMs, search engines, and knowledge graphs) uses the annotation to decide whether to absorb your content at recruitment (Gate 6). Each has different criteria, so you need to assess your own content for its “annotatability” in the context of all three. And a small but telling detail: Back in 2020, Martin Splitt suggested that Google compares your meta description to its own LLM-generated summary of the page. When they match, the system’s confidence in its page-level understanding increases, and that confidence cascades into better annotation scores for every chunk — one of thousands of tiny signals that accumulate. Annotation is the key midpoint of the 10-gate pipeline, where the scoreboard turns on. Everything before it is infrastructure: “Can the system access and store your content?” Everything after it is competition: When you consider what happens at the annotation gate and its depth, links and keywords become the wrong lens entirely. They describe how you tried to influence a ranking system, whereas annotation is the mechanism behind how the algorithmic trinity chooses the content that builds its understanding of what you are. The frame has to shift. You’re educating algorithms. They behave like children, learning from what you consistently, clearly, and coherently put in front of them. With consistent, corroborated information, they build an accurate understanding. Given inconsistent or ambiguous signals, they learn incorrectly and then confidently repeat those errors over time. Building confidence in the machine’s understanding of you is the most important variable in this work, whether you call it SEO or AAO. “Confiance” (confidence) is the signal that drives how systems understand content. Slide from my SEOCamp Lyon 2017 presentation. In 2026, every AI assistive engine and agent is that same child, operating at a greater scale and with higher stakes than Google ever had. Educating the algorithms isn’t a metaphor. It’s the operational model for everything that follows. For a more academic perspective, see: “Annotation Cascading: Hierarchical Model Routing, Topical Authority, and Inter-Page Context Propagation in Large-Scale Web Content Classification.” 5 levels of annotation: 24+ dimensions classifying your content at Gate 5 When mapping the annotation dimensions, I identified 24, organized across five functional categories. After presenting this to Canel, his response was: “Oh, there is definitely more.” Of course there are. This taxonomy is built through observation first, then naming what consistently appears. The [know/guess] distinctions follow the same logic: test hypotheses, eliminate what doesn’t hold up, and keep what remains. The five functional categories form the foundation of the model. They are simple by design — once you understand the categories, the dimensions follow naturally. There are likely additional dimensions beyond those mapped here. What follows is the taxonomy: the categories are directionally sound (as confirmed by Canel), while the specific dimension assignments reflect observed behavior and remain incomplete. Level 1: Gatekeepers (eliminate) Temporal scope, geographic scope, language, and entity resolution. Binary: pass or fail. If your content fails a gatekeeper (wrong language, wrong geography, or ambiguous entity), it is eliminated from that query’s candidate pool instantly. The other dimensions don’t come into play. Level 2: Core identity (define) Entities, attributes, relationships, sentiment. This is where the system decides what your content means: Who is being discussed. What facts are stated. How entities relate. What the tone is. Without clear core identity annotations, a chunk carries no semantic weight in any downstream gate. Level 3: Selection filters (route) Intent category, expertise level, claim structure, and actionability. These determine which competition pool your content enters. Is this informational or transactional? Beginner or expert? Wrong pool placement means competing against content that is a better match for the query, and you’ve lost before recruitment or ranking begins. Level 4: Confidence multipliers (rank) Verifiability, provenance, corroboration count, specificity, evidence type, controversy level, and consensus alignment. These scale your ranking within the pool. This is where validated, corroborated, and specific content outranks accurate but unvalidated content. The multipliers explain why a well-sourced third-party article about you often outperforms your own claims: provenance and corroboration scores are higher. Confidence has a multiplier effect on everything else and is the most powerful of all signals. Full stop. Level 5: Extraction quality (deploy) Sufficiency, dependency, standalone score, entity salience, and entity role. These determine how your content appears in the final output. Is this chunk a complete answer, or does it need context? Is your entity the subject, the authority cited, or a passing mention? Extraction quality determines whether AI quotes you, summarizes you, or ignores you. Across all five levels, a confidence score is attached to every individual annotation. Not just what the system thinks your content means, but how certain it is. Clarity drives confidence. Ambiguity kills it. Canel also confirmed additional dimensions I had not initially mapped: audience suitability, ingestion fidelity, and freshness delta. These sit across the existing categories rather than forming a sixth level. In 2022, Splitt named three annotation behaviors in a Duda webinar that map directly onto the five-level model. The centerpiece annotation is Level 2 in direct operation: “We have a thing called the centerpiece annotation,” Splitt confirmed, a classification that identifies which content on the page is the primary subject and routes everything else — supplementary, peripheral, and boilerplate — relative to it. “There’s a few other annotations” of this type, he noted. Annotation runs before recruitment, which means a chunk classified as non-centerpiece carries that verdict into every gate that follows. Boilerplate detection is Level 3: content that appears consistently across pages — headers, footers, navigation, and repeated blocks — enters a different competition pool based on its structural role alone. “We figure out what looks like boilerplate and then that gets weighted differently,” Splitt said Off-topic routing closes the picture. A page classified around a primary topic annotates every chunk relative to that centerpiece, and content peripheral to the primary topic starts its own competition pool at a disadvantage before Recruitment begins. Splitt’s example: a page with 10,000 words on dog food and a thousand on bikes is “probably not good content for bikes.” The system isn’t ignoring the bike content. It’s annotating it as peripheral, and that annotation is the routing decision. Get the newsletter search marketers rely on. See terms. The multiplicative destruction effect: When one near-zero kills everything In Sydney in 2019, I was at a conference with Gary Illyes and Brent Payne. Illyes explained that Google’s quality assessment across annotation dimensions was multiplicative, not additive. Illyes asked us not to film, so I grabbed a beer mat and noted a simple calculation: if you score 0.9 across each of 10 dimensions, 0.9 to the power of 10 is 0.35. You survive at 35% of your original signal. If you score 0.8 across 10 dimensions, you survive at 11%. If one dimension scores close to zero, the multiplication produces a result close to zero, regardless of how well you score on every other dimension. Payne’s phrasing of the practical implication was better than mine: “Better to be a straight C student than three As and an F.” The beer mat went into my bag. The principle became central to everything I’ve built since. The multiplicative destruction effect has a direct consequence for annotation strategy: the C-student principle is your guide. A brand with consistently adequate signals across all 24+ dimensions outperforms a brand with brilliant signals on most dimensions and a near-zero on one. The near-zero cascades. A gatekeeper failure (Level 1) eliminates the content entirely. A core identity failure (Level 2) misclassifies it so badly that high confidence multipliers at Level 4 are applied to the wrong entity. An extraction quality failure (Level 5) produces a chunk that the system can retrieve but can’t deploy usefully. The failure doesn’t have to be dramatic to be fatal. At the annotation stage, misclassification, low confidence, or near-zero on one dimension will kill your content and take it out of the race. Nathan Chalmers, who works at Bing on quality, told me something that puts this in a different light entirely. Bing’s internal quality algorithm, the one making these multiplicative assessments across annotation dimensions, is literally called Darwin. Natural selection is the explicit model: content with near-zero on any fitness dimension is selected against. The annotations are the fitness test. The multiplicative destruction effect is the selection mechanism. How annotation routes content to specialist language models The system doesn’t use one giant language model to classify all content. It routes content to specialized small language models (SLMs): domain-specific models that are cheaper, faster, and paradoxically more accurate than general LLMs for niche content. A medical SLM classifies medical content better than GPT-4 would, because it has been trained specifically on medical literature and knows the entities, the relationships, the standard claims, and the red flags in that domain. What follows is my model of how the routing works, reconstructed from observable behavior and confirmed principles. The existence of specialist models is confirmed. The specific cascade mechanism is my reconstruction. The routing follows what I call the annotation cascade. The choice of SLM cascades like this: Site level (What kind of site is this?) Refined by category level (What section?) Refined by page level (what specific topic?) Applied at chunk level (What does this paragraph claim?) Each level narrows the SLM selection, and each level either confirms or overrides the routing from above. This maps directly to the wrapper hierarchy from the fourth piece: the site wrapper, category wrapper, and page wrapper each provide context that influences which specialist model the system selects. The system deploys three types of SLM simultaneously for each topic. This is my model, derived from the behavior I have observed: annotation errors cluster into patterns that suggest three distinct classification axes. The subject SLM classifies by subject matter — what is this about? — routing content into the right topical domain. The entity SLM resolves entities and assesses centrality and authority: who are the key players, is this entity the subject, an authority cited, or a passing mention? The concept SLM maps claims to established concepts and evaluates novelty, checking whether what the content asserts aligns with consensus or contradicts it. When all three return high confidence on the same entity for the same content, annotation cost is minimal, and the confidence score is very high. When they disagree (i.e., the subject SLM says “marketing,” but the entity SLM can’t resolve the entity, and the concept SLM flags the claims as novel), confidence drops, and the system falls back to a more general, less accurate model. The key insight? LLM annotation is the failure mode. The system wants to use a specialist. It defaults to a generalist only when it can’t route to a specialist. Generalist annotation produces lower confidence across all dimensions. The practical implication Content that’s category-clear within its first 100 words, uses standard industry terminology, follows structural conventions for its content type, and references well-known entities in its domain triggers SLM routing. Content that’s topically ambiguous or terminologically creative gets the generalist. Lower confidence propagates through every downstream gate. Now, this may not be the exact way the SLMs are applied as a triad (and it might not even be a trio). However, two things strike me: Observed outputs act that way. If it doesn’t function this way, it would be. First-impression persistence: Why the initial annotation is the hardest to correct Here is something I’ve observed over years of tracking annotation behavior. It aligns with a principle Canel confirmed explicitly for URL status changes (404s and 301 redirects): the system’s initial classification tends to stick. When the bot first crawls a page, it selects an SLM, runs the annotation, assigns confidence scores, and saves the classification. The next time it crawls the same page, it logically starts with the previously assigned model and annotations. I call this first-impression persistence. The initial annotation is the baseline against which all subsequent signals are measured. The system doesn’t re-evaluate from scratch. It checks whether the new crawl is consistent with the existing classification, and if it is, the classification is reinforced. Canel confirmed a related mechanism: when a URL returns a 404 or is redirected with a 301, the system allows a grace period (very roughly a week for a page, and between one and three months for content, in my observation) during which it assumes the change might revert. After the grace period, the new state becomes persistent. I believe the same principle applies to content classification: a window of fluidity after first publication, then crystallization. I have direct evidence for the correction side from the evolution of my own terminologies. When I first described the algorithmic trinity, I used the phrase “knowledge graphs, large language models, and web index.” Google, ChatGPT, and Perplexity all picked up on the new term and defined it correctly. A month later, I changed the last one to “search engine” because it occurred to me that the web index is what all three systems feed off, not just the search system itself. At the point of correction, I had published roughly 10 articles using the original terminology. I went back and invested the time to change every single one, updating every reference, leaving zero traces. A month later, AI assistive engines were consistently using “search engine” in place of “web index.” The lesson is that change is possible, but you need to be thorough: any residual contradictory signal (one old article, one unchanged social post, and one cached version) maintains inertia proportionally. Thoroughness is the unlock, rather than time. A rebrand, career pivot, or repositioning is the practical example. You can change the AI model’s understanding and representation of your corporate or personal brand, but it requires thoroughly and consistently pivoting your digital footprint to the new reality. In my experience, “on a sixpence” within a week. I’ve done this with my podcast several times. Facebook achieved the ultimate rebrand from an algorithmic perspective when it changed its name to Meta. The practical implication Get your annotation right before you publish. The first crawl sets the baseline. A page published prematurely (with an unclear topic or ambiguous entity signals) crystallizes into a low-confidence annotation, and changing it later requires significantly more effort than getting it right the first time. Annotation-time grounding: The bot cross-references three sources while classifying your content The system doesn’t annotate in a vacuum. When the bot classifies your content at Gate 5, it cross-references against at least three sources simultaneously. This is my model of the mechanism. The observable effect — that annotation confidence correlates with entity presence across multiple systems — is confirmed from our tracking data. The bot carries prioritized access to the web index during crawling, checking your content against what it already knows: Who links to you. What context those links provide. How your claims relate to claims on other pages. Against the knowledge graph, it checks annotated entities during classification — an entity already in the graph with high confidence means annotation inherits that confidence, while absence starts from a much lower baseline. The SLM’s own parametric knowledge provides the third cross-reference: each SLM compares encountered claims against its training data, granting higher confidence to claims that align, flagging contradictions, and giving lower confidence to novel claims until corroboration accumulates. This means annotation quality isn’t just about how well your content is written. It’s about how well your entity is already represented across all three of the algorithmic trinity. An entity with strong knowledge graph presence, authoritative web index links, and consistent SLM-domain representation gets higher annotation confidence on new content automatically. The flywheel: better presence leads to better annotation, which leads to better recruitment, which strengthens presence, and which improves future annotation. Once again, better to have an average presence in all three than to have a dominant presence in two and no presence in one. And this is why knowledge graph optimization (what I’ve been advocating for over a decade) isn’t separate from content optimization. They are the same pipeline. Your knowledge graph presence directly improves how accurately, verbosely, and confidently the system annotates every new piece of content you publish. If you’re thinking “Knowledge graph? That’s just Google,” think again. In November 2025, Andrea Volpini intercepted ChatGPT’s internal data streams and found an operational entity layer running beneath every conversation: structured entity resolution connected to what amounts to a product graph mirroring Google Shopping feeds. OpenAI is building its own knowledge graph inside the LLM. My bet is that they will externalize it for several reasons: a knowledge graph in an LLM doesn’t scale, an LLM will self-confirm, so the value is limited, a standalone knowledge graph can be easily updated in real time without retraining the model, and it’s only useful at scale when it stays current. The algorithmic trinity isn’t a Google phenomenon. It’s the architectural pattern every AI assistive engine and agent converges on, because you can’t generate reliable recommendations without a concept graph, structured entity data, and up-to-date search results to ground them. Why Google and Bing annotate differently from engines that rent their index Google and Bing own their crawling infrastructure, indexes, and knowledge graphs. They can afford grace periods, schedule rechecks, and maintain temporal state for URLs and entities over months. OpenAI, Perplexity, and every engine that rents index access from Google or Bing operate on a fundamentally different model. They have two speeds: A slow Boolean gate (Does this content exist in the index I have access to?) A fast display layer (What does the content say right now when I fetch it for grounding?) The Boolean gate inherits Google’s and Bing’s annotations. Whether your content appears at all depends on whether it was recruited from the index those engines draw from, and that recruitment depends on annotation and selection decisions made by the algorithmic trinity. But what these engines show when they cite you is fetched in real time. The practical implication For Google and Bing, you’re optimizing for annotation quality with the benefit of grace periods and gradual reclassification. For engines that don’t own their index, the Boolean presence is inherited from the rented index and is slow to change, but the surface-level display changes every time they re-fetch. That means what you are seeing in the results is not a direct measure of your annotation quality. It’s a snapshot of your page at the moment of fetch, and those two things may have nothing to do with each other. How to optimize for annotation quality: The six practical principles The SEO industry has spent two decades optimizing for search and assistive results — what happens after the system has already decided what your content means. We should be optimizing for annotation. If the annotation is wrong, everything downstream suffers. When the annotation is accurate, verbose, and confident, your content has a significant advantage in recruitment, grounding, display, and, ultimately, won. 1. Trigger SLM routing Make your topic category obvious within the first 100 words. Use standard industry terminology. Follow structural conventions. Reference well-known entities. The goal: specialist model, not generalist. 2. Write for all three SLMs Clear signals for subject (what is this about?), entity (who is the authority?), and concept (what established ideas does this connect to?). Ambiguity on any axis reduces confidence. 3. Get it right before publishing First-impression persistence means the initial annotation is the hardest to change. Publish only when topic, entity signals, and claims are unambiguous. 4. Build the flywheel Knowledge graph presence, web index centrality, LLM parameter strengthening, and correct SLM-domain representation all feed annotation confidence for new content. Invest in entity foundation, and every future piece benefits from inherited credibility. 5. Eliminate noise when correcting Change every reference. Leave zero contradictory signals. Noise maintains inertia proportionally. 6. Audit for annotation, not just indexing A page can be indexed and still misannotated. If the AI response is wrong about you, the problem is almost certainly at Gate 5, not Gate 8. Annotation is the gate where most brands silently lose. The SEO industry doesn’t yet have a vocabulary for it. That needs to change, because the gap between brands that get annotation right and brands that don’t is the gap between consistent AI visibility and permanent algorithmic obscurity. 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 Why annotation matters so much and why it should be your main focus You’ve done everything within your power to create the best possible content that maps to intent of your ideal customer profile, you have methodically optimized your digital footprint, your data feeds every entry mode simultaneously: pull, push discovery, push data, MCP, and ambient, so they are all drawing from the same clean, consistent source So, content about your brand has passed through the DSCRI infrastructure phase, survived the rendering and conversion fidelity boundaries, and arrived in the index (Gate 4) intact. Phew! Now it gets classified. Annotation is the last moment in the pipeline where you have the field to yourself. Every decision in DSCRI was absolute: you vs. the machine, with no competitor in the frame. Annotation is still absolute. The system classifies your content based on your signals alone, independently of what any competitor has done. Nobody else’s data changes how your entity is annotated. But this is the last time you aren’t competing. From recruitment onward, everything is relative. The field opens, every brand that passed annotation enters the same competitive pool, and the advantage you carried through the absolute phase becomes your starting position in the competitive race you have to win. That means: Get annotation right, and you start ahead, with confidence that compounds through every downstream gate in RGDW. Get it wrong, and the multiplicative destruction effect does its work — a near-zero on one annotation dimension cascades through recruitment, grounding, display, and won. No amount of excellent content, structural signals, or entry-mode advantage recovers it. Warning: First-impression persistence (remember, the first time you are annotated is the baseline) means you don’t get a clean retry. Changing the baseline requires thoroughness, time, and more effort than getting it right on the first crawl. Annotation isn’t the gate that most brands focus on. It’s the gate where most brands silently lose. This is the eighth piece in my AI authority series. The first, “Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it,” introduced cascading confidence. The second, “AAO: Why assistive agent optimization is the next evolution of SEO,” named the discipline. The third, “The AI engine pipeline: 10 gates that decide whether you win the recommendation,” mapped the full pipeline. The fourth, “The five infrastructure gates behind crawl, render, and index,” walked through the infrastructure phase. The fifth, “5 competitive gates hidden inside ‘rank and display’,” covered the competitive phase. The sixth, “The entity home: The page that shapes how search, AI, and users see your brand,” mapped the raw material. The seventh, “The push layer returns: Why ‘publish and wait’ is half a strategy,” extended the entry model. Up next: “The engine’s recruitment decision: What topical ownership actually means.” View the full article

It’s hard to get real-world information about what jobs pay. Online salary websites are often inaccurate, and people can get weird when you ask them directly. So to take some of the mystery out of salaries, it’s the annual Ask a Manager salary survey. Fill out the form below to anonymously share your salary and other relevant info. (Do not leave your info in the comments section! If you can’t see the survey questions, try this link instead.) When you’re done, you can view all the responses in a sortable spreadsheet. Loading… The post how much money do you make? appeared first on Ask a Manager. View the full article

Market performance tends to dominate the conversation about risks to a retirement plan. But spending shocks can also curb a retirement portfolio’s longevity. In Morningstar’s research, we examined the implications of two major types of spending shocks: unanticipated early retirement and uninsured long-term care expenses at the end of life. The former may necessitate spending over a longer period, often with higher healthcare costs in the pre-Medicare years, while the latter can translate into an effective “balloon payment” toward the end of life. Early retirement Early retirement — before the standard age of 65 — is an increasingly common scenario. While Social Security’s full retirement age is currently between 66 and 67, the average retirement age is 62, according to a study from MassMutual. That’s corroborated by Social Security filing data, which show that roughly 25% of retirees take Social Security when it’s first available at age 62, and 15% file at 63 or 64. Nearly half of the retirees surveyed by MassMutual said they had retired earlier than planned; commonly cited reasons included layoffs, being able to retire sooner than expected, or illness or injury. Early retirement has significant implications for retirement spending, with longer drawdown periods necessitating lower spending to maintain a high likelihood of not running out later on. In our base-case spending simulation, expanding the drawdown period from 30 to 35 years reduces the starting safe withdrawal rate from 3.9% to 3.5%. Stretching the time spending horizon to 40 years takes the starting safe withdrawal rate to 3.2%. Keeping withdrawals low in early retirement may be challenging on a few levels, however. First, individuals aren’t eligible for Medicare coverage until age 65, so bridging healthcare coverage in the intervening years has the potential to increase spending. Insurance coverage for 62- to 65-year-olds from the ACA marketplace averaged between $800 and $1,200 a month in 2025, according to data from Boldin. Meanwhile, Cobra coverage (extending workplace-provided coverage) for people 62 to 65 averaged $700 to $1,500 a month. For a 62-year-old taking a safe withdrawal rate of 3.5% ($35,000) from her $1 million portfolio, healthcare costs would consume roughly a third of those withdrawals. Further complicating matters for young retirees is that many individuals wish to delay Social Security to increase their eventual benefits. At the same time, delaying Social Security can necessitate higher withdrawals in the early part of retirement, thereby imperiling the portfolio’s ability to last over the longer time horizon. Long-term care spending Just as early retirement can cause a spending shock at the front end of retirement, long-term care costs can prompt a spending shock later in life. A 2025 report authored by Spencer Look and Jack VanDerhei of the Morningstar Center for Retirement & Policy Studies found that 43% of baby boomers will incur long-term care costs, with the average cost of that care $242,373. The likelihood of needing care correlates with longevity: While just 24% of men and 27% of women who die at age 75 will require long-term care, 52% of men and 60% of women who die at age 95 will require long-term care. Incurring sizable long-term care costs can have catastrophic effects for a financial plan: The Morningstar study found that when long-term care costs are included in the analysis of the viability of retirement assets, 41% of older-adult households that incur long-term care costs are likely to run out of funds. Older adults can take different approaches to address this risk. They might set aside a separate long-term care “bucket,” separate from their spending portfolios. Others may plan to use home equity. Alternatively, those with very tight finances might create a spending plan to cover their costs during their healthy years, then rely on government resources if they require long-term care after that. The final option for handling the cost of long-term care is to build it into the spending plan, spending less throughout retirement to account for the possibility of a spike later in life. To help model a long-term care shock, we assumed spending in years 29 and 30 to be twice what spending was in year 28. Factoring in that type of shock, the starting safe withdrawal percentage for the person retiring and claiming Social Security at age 67 is 3.5%, versus 3.9% for our base case without that shock. This article was provided to The Associated Press by Morningstar. For more retirement content, go to https://www.morningstar.com/retirement. ChristineBenz is director of personal finance and retirement planning for Morningstar and co-host of The Long View podcast. Related Links What to Do in the Five Years Before You Retire https://www.morningstar.com/retirement/emily-guy-birken-what-do-five-years-before-you-retire What You Need to Know About Annuities https://www.morningstar.com/retirement/what-you-need-know-about-annuities 10 Sources of Emergency Cash, Ranked From Best to Worst https://www.morningstar.com/personal-finance/10-sources-emergency-cash-ranked-best-worst —Christine Benz of Morningstar View the full article

Iranians fear The President’s threats against “whole civilization” are part of an onslaught on fabric of nationView the full article

I’ve heard it too many times to count, “We’ve never done PR before and are getting ready to announce [insert your major milestone of choice]” Too often, businesses wait until they have big news to begin thinking about strategic communications. They’re about to close a funding round, launch a product, or enter a new market. But here’s the thing: If you’re just starting to think about PR now, you’re already behind. After nearly 20 years leading communications for fintech companies and financial institutions, I can confidently say that the organizations that benefit most from major announcements began building visibility long before the moment arrived. WHY COMPANIES START TOO LATE Teams often assume their news will attract attention. That their announcement will prove their credibility. That the story will tell itself with the build it and they will come mentality. That’s seldom how it works. The mistake is assuming trust can be built on the same timeline as attention. These moments reveal whether credibility already exists. Psychologists call this tendency to underestimate how long it takes to build something meaningful the planning fallacy. In business, it shows up in many ways. One example is believing that trust can be established the moment people start paying attention. Think about your own reaction to news. When a company you’ve never heard of announces a major milestone, you might ask “Who is this?” and quickly return to scrolling. But when a company you’ve seen and heard about shares similar news, your reaction is different. That difference is momentum. 5 SIGNS IT’S TIME TO INVEST IN PR So how do you know when it’s time to invest in PR? Here are five signs. 1. You have something to say. One of the clearest signs a company is ready for PR is having a point of view. You need to have something to say and be able to connect it to what you do. That means being able to explain what’s changing, what’s broken, and why your approach matters now. It requires proof points and substance, not just commentary. This matters even more in the age of AI search and discovery. Research from Muck Rack shows generative AI relies heavily on earned media, making media validation essential for modern brand discovery. Your narrative will take shape with or without you. The question is whether you’re actively taking steps to shape it. 2. You know your story and what makes you different. Companies ready for PR can clearly articulate what makes them different, why it matters, and how it connects to their broader goals. Without that clarity, early communications efforts often feel reactive and fragmented. With it, every media interaction reinforces the same story. In a conversation with me, Shahzeb Khan, head of marketing for the Americas at Amdocs, put it this way: “A fundamental question I ask myself is whether our story is differentiated and not just noise, credible and backed by facts and observations, and whether it advances a meaningful perspective in the market and supports a strategic business priority.” 3. Leadership is aligned and willing to commit. PR cannot succeed as a side project. Crucially, it requires leadership participation. Khan noted that leadership alignment can be the catalyst for thought leadership. But that thought leadership must come from somewhere—from executives willing to offer perspective, speak with media, and contribute to industry conversations. Without that participation, momentum stalls before it starts. And it must be a long-term commitment. PR isn’t a one-time campaign. Once you’re ready for it, it becomes an ongoing part of how the company communicates with the market. 4. Your team can support the attention you create. You also need to ask whether your organization is prepared to support the attention PR creates. Do you have a strong website? Clear messaging? A responsive sales team? Supporting content? And can you move quickly when opportunities appear? Kenon Chen, EVP of strategy and growth at Clear Capital, described to me the moment he knew they were ready: “We had a marketing team ready to support and capitalize on successful PR activities. The right team ready to go after a bigger mission is the perfect time to lean into proactive communications.” 5. Your audience encompasses more than just customers. Eventually, growth depends on influencing decision-makers beyond your customer base—analysts, investors, media, partners, and regulators. “After clearly defining our three-year company strategy, we realized that we needed [to have] greater influence on multiple industry stakeholders beyond potential clients to achieve our goals,” said Chen. MOMENTUM DOESN’T WAIT These five signs don’t all appear at once. When most are true, that’s your moment. The mistake is waiting for perfect conditions that never arrive. Momentum builds through consistency, not perfection. The companies whose stories are heard? They invested in communications before they had big news to share.Grace Keith Rodriguez is the CEO at Caliber Corporate Advisers. View the full article

In his news conference Monday, President Donald The President threatened to blow up every bridge and power plant in Iran, action that would be so far-reaching that some experts in military law said it could constitute a war crime. The issue could turn on whether the power plants were legitimate military targets, whether the attacks were proportional compared with what Iran has done and whether civilian casualties were minimized. The President’s threat was so broad it did not seem to account for the harm to civilians, prompting Democrats in Congress, some United Nations officials and scholars in military law to say such strikes would violate international law. The president’s eventual actions often fall short of his all-encompassing rhetoric in the moment, but his warnings about the power plants and bridges were unambiguous both on Sunday and Monday as he set a deadline of Tuesday night for Iran to open the Strait of Hormuz. A spokesman for U.N. Secretary-General Antonio Guterres on Monday warned that attacking such infrastructure is banned under international law. “Even if specific civilian infrastructure were to qualify as a military objective,” Stephane Dujarric said, an attack would still be prohibited if it risks “excessive incidental civilian harm.” Rachel VanLandingham, a Southwestern Law School professor who served as a judge advocate general in the U.S. Air Force, said civilians are likely to die if power is cut to hospitals and water treatment plans. “What The President is saying is, ‘We don’t care about precision, we don’t care about impact on civilians, we’re just going to take out all of Iranian power generating capacity,'” the retired lieutenant colonel said. Shipping in the Strait of Hormuz, a chokepoint in the Persian Gulf through which 20% of the world’s oil normally flows, has been all but halted, sending oil prices soaring and roiling the stock market. The President said Monday that he’s “not at all” concerned about committing war crimes as he continues to threaten destruction. He also warned that every power plant will be “burning, exploding and never to be used again.” “I hope I don’t have to do it,” The President added. When asked for further comment Monday, White House spokeswoman Anna Kelly said “the Iranian people welcome the sound of bombs because it means their oppressors are losing.” “The Iranian regime has committed egregious human rights abuses against its own citizens for 47 years, just murdered tens of thousands of protestors in January, and has indiscriminately targeted civilians across the region in order to cause as much death as possible throughout this conflict,” Kelly wrote in an email. ‘Clearly a threat of unlawful action’ As the conflict has entered its second month, The President has escalated his warnings to bomb Iran’s infrastructure, including Kharg Island, central to Iran’s oil industry, and desalination plans that provide drinking water. In a Truth Social post on March 30, The President warned that the U.S. would obliterate “all of their Electric Generating Plants, Oil Wells and Kharg Island (and possibly all desalinization plants!), which we have purposefully not yet ‘touched.'” On Easter Sunday, The President threatened in an expletive-laden post that Iran will face “Power Plant Day, and Bridge Day, all wrapped up in one,” while adding that “you’ll be living in Hell” unless the strait reopens. “This strikes me as clearly a threat of unlawful action,” said Michael Schmitt, a professor emeritus at the U.S. Naval War College and an international law professor at the University of Reading in Britain. A power facility can be attacked under the laws of armed conflict if it provides electricity to a military base in addition to civilians, Schmitt said. But the strike must not “cause disproportionate harm to the civilian population, and you’ve done everything to minimize that harm.” Harm does not include inconvenience or fear, said Schmitt, who has taught military commanders. But it does mean severe mental suffering, physical injury or illness. Schmitt said military commanders should consider alternatives, such as targeting a substation or transmission lines that feed electricity to a base, before destroying an entire power plant. “If you look at the operation and you’ve got a valid military objective, but it’s going to cause harm to civilians and you go, ‘Whoa, that’s a lot,’ then you should stop,” Schmitt said. “If you hesitate to take the shot, don’t take the shot.” ‘He’s using that leverage’ Republican Sen. Joni Ernst of Iowa said Monday that The President is “absolutely not” threatening a war crime when he said he might bomb civilian infrastructure. The infrastructure is also used by the military, Ernst said, and “it’s an ongoing operation.” “If he needs leverage, he’s using that leverage,” she said while presiding over a brief pro forma session of the Senate. But Democratic Sen. Chris Van Hollen of Maryland, also in the Capitol for the brief session, said it would be a “textbook war crime.” “If you target civilian infrastructure for the purposes the president was talking about, it clearly is a war crime,” Van Hollen said. Dujarric, the U.N. spokesman, said the question of whether attacks on civilian infrastructure would be considered war crimes would have to be decided by a court. However, Katherine Thompson, a senior fellow in defense and foreign policy studies at the Cato Institute, a libertarian think tank, said any accountability would more likely come from Congress. She said thinking otherwise would mean believing that the U.S. would allow its president to be held accountable by foreign entities. “This is the persnickety, inconvenient truth about international law: It only works if sovereign nations are willing to cede their sovereignty to a foreign body for accountability,” she said. But Congress would have to say the president has gone too far. And then both houses would have to take action and with enough support to overcome a presidential veto, a highly unlikely prospect. The President also appears to have broad legal immunity under the Supreme Court’s ruling in the criminal case before his reelection, said VanLandingham. And the president could also grant preemptive pardons to top officials if needed. ‘We’re giving them a gift’ Even if technically justified under the law of war, strikes that bring harm to civilians could backfire for the U.S. long term, VanLandingham said. “There’s a lot of violence that can still be justified as lawful, but lawful can still be awful,” VanLandingham said. “How far did that get us in Iraq? How far did that get us in Afghanistan? How far did that get us in Vietnam?” The President’s rhetoric risks spreading fear among regular Iranians and communicating that the U.S. isn’t worried about their well-being, VanLandingham said. The country’s leaders could use it as propaganda to create and harden opposition, contributing to a longer, tougher war. Associated Press writers Farnoush Amiri and Edith M. Lederer in New York and Mary Clare Jalonick and Seung Min Kim in Washington contributed to this report. —Ben Finley, Lindsay Whitehurst and Gary Fields, Associated Press View the full article

Non-bank lenders rapidly reduce holdings of EM debt during shocks such as the Iran war, analysis suggestsView the full article

Short positions rise sharply as traders eye economic fallout from Iran warView the full article

Whether you’re doomscrolling on LinkedIn or talking to friends, AI-induced job loss anxiety feels inescapable right now. As companies go full throttle on investing in automation tools, the fear that entire roles can be instantly eliminated feels very real. After the surge in economic activity and tech adoption during the pandemic, tech companies issued mass layoffs after over-expanding. That trend continued in the last few months, with tech giants like Amazon and Oracle laying off thousands of employees. But there have been a few silver linings in the mostly pessimistic discourse around AI and the future of work: A recent surprising bright spot in hiring right now for software engineers. Business Insider reported that companies are hiring more software engineers, with software job listings climbing 30% so far this year. According to tech hiring firm TrueUp—whose data tracks more than 260,000 open roles across 9,000 tech startups and public tech firms—more than 67,000 software engineering job openings. After a stretch of hiring freezes and pervasive layoffs, this might feel like renewed momentum for some, especially since the recent jobs report issued on Friday was more optimistic than expected. “Encouraging to see tech hiring gaining momentum again, especially amid ongoing conversations about AI‑driven job displacement,” one insurance professional commented on LinkedIn. “The data reinforces an important point: while AI is reshaping roles, it’s also creating new opportunities that require human expertise, adaptability, and strategic thinking.” In the same vein, an engineer and AI founder wrote: “It’s getting increasingly cheaper to build custom solutions, which means we might end up with much, much more code that needs to be reviewed and maintained.” Others shared less optimism about the statistic. “The data is real[,] but what the headline does not say is that the jobs driving that number are AI-fueled,” a senior recruiter wrote. “The same quarter that produced this hiring surge also saw 52,050 tech job cuts announced, the worst Q1 since 2023, with AI cited as the leading reason for layoffs across industries.” Another LinkedIn user pointed out that job openings do not equate to jobs filled. “Saying ‘AI isn’t killing jobs’ because software engineering openings are up is like saying the housing market is fine because penthouse listings are booming,” one consultant wrote. Coaching company Challenger, Gray & Christmas reported that the tech sector announced 18,720 job cuts in March, and predicted more layoffs to come. Following a bleak few months and the loss of 133,000 jobs in February, the most recent jobs report showed that the U.S. added 178,000 jobs in March, offering a bit of much-needed motivation. AI has completely jolted entry-level roles and internships meant to help young workers kickstart their careers. The unemployment rate for recent college graduates reached 5.6% in December. Even with the increase in job openings for software engineers across the tech sector, young and eager professionals might not feel the reprieve they’re expecting. As companies look to invest in AI, the talent pool—especially among entry-level applicants—has grown considerably, making these available jobs feel more competitive. More job openings don’t necessarily mean job hunting is going to get easier, especially when the bar for skill is getting higher. View the full article

I've been using Pixel phones every day for several years at this point, so I thought I'd discovered every secret menu and hidden feature these Google handsets have to offer—but it turns out I was wrong. Make Use Of enlightened me about the diagnostic tool built into Google Pixels, hidden away behind the number pad of the phone app. Its official name is the Pixel Repair Diagnostics App, and according to Google, it's built into every Pixel phone and tablet. It gives you a dashboard for testing just about every part of your phone's setup, from Bluetooth connections to camera sensors. The Pixel Repair Diagnostics App. Credit: Lifehacker To get to the diagnostics tool, open up the Phone app on your Pixel, switch to the Keypad screen, then type *#*#7287#*#*. You'll be asked if you have reliable wifi, so press Confirm, and you'll get into the app proper—with the screen brightness ramped right up. You can choose to work through these diagnostic tests individually, run related tests together via the Check Group options, or test everything via the Start Test button that appears at the top. The three-dot menu up in the top-right corner gives you access to results for tests that have already been run. There's a lot to work through here: The Visual group alone includes tests for Physical Damage, Display Defects, Backglass Defects, and Camera Defects. Each test differs in terms of what you need to do—so for Physical Damage it's simply a case of checking around your phone, whereas for WiFi the phone will itself try and get online and see if the connection is stable. Some diagnostics require more interactionSome of these tests require more interaction than others. For Light Sensor for example, you'll be asked to cover your phone's light sensor with your hand (it's usually up at the top of the screen next to the selfie camera) while a reading is taken. For Gyroscope, you need to move your phone in a figure-of-8 pattern. When it comes to Display under Screen, you get shown a series of images—some solid colors, others with writing on them—so you can carefully examine the screen and look for any inconsistencies or defects. It's then up to you to either choose Pass or Fail. Also under the Screen heading there's Touch Panel, a test that tasks you with performing various taps and swipes—one of the actions you have to do is use three fingers to drag some colored balls down the screen. The aim is to make sure every part of the display remains responsive. You'll need to manually confirm certain tests have been passed. Credit: Lifehacker For Microphone under Audio, your phone will play a little jingle and attempt to record it through all of the mics your phone has, at the same time. Each microphone recording is then played back, and it's up to you to confirm that they all worked. The Front Camera and Rear Camera tests under Camera are particularly useful, because they test each individual camera in turn by capturing photos and videos from them—so if your phone has three cameras around the back that are normally used in unison, you can separate and test them all individually. This is a useful tool to turn to whenever you think something might be broken on your phone—and if there's a problem, it will tell you where the problem lies. You can quit the app like any other, with a swipe up from the bottom of the screen (or by pressing the home button, if you're using button navigation). View the full article

Many of today’s PPC tools were designed to be easily accessible to ecommerce. That doesn’t mean lead gen can’t take advantage of them, but it does mean more intentional application is required. Lead gen with AI still requires a creative approach, and many conventional ecommerce tools still apply — but not always in the same way. Here are the priorities that matter most for succeeding with lead gen using AI. Disclosure: I’m a Microsoft employee. While this guidance is platform-agnostic, I’ll reference examples that lean into Microsoft Advertising tooling. The principles apply broadly across platforms. 1. Fix your conversion data first This is the single most important thing you can do as AI becomes more embedded in media buying. Between evolving attribution models, privacy changes, different platform connections, and shifts in how consumers engage with brands, it’s reasonable to ask whether your data is still telling an accurate story. Start by auditing your CRM or lead management system. Make sure the data you pass back to advertising platforms is clean, consistent, and intentional. In most cases, data issues stem from human choices rather than technical failures. Still, there are a few technical checks that matter: Confirm conversions are firing consistently. Regularly review conversion goal diagnostics. Validate that lead status updates and downstream signals are actually flowing back. If AI systems are learning from your data, you want to be confident that the feedback loop reflects reality. Dig deeper: How to make automation work for lead gen PPC 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 2. Make landing pages easy to ingest and easy to understand Lead gen campaigns often have multiple conversion paths, which can be helpful for users. But from an AI perspective, ambiguity is a risk. Your landing pages should make it clear: What action you want the user to take. What happens after action is taken. Which conversions matter most. Redundant or unclear conversion paths can confuse both users and systems. If AI crawlers detect that anticipated outcomes are inconsistent, they may begin to question the accuracy of what your site claims to do. That can limit eligibility for certain placements. Language clarity matters just as much. Avoid jargon, eccentric terminology, or internally focused phrasing when describing your services. Clear, plain language makes it easier for AI systems to understand who you are, what you offer, and how to match creative to the right audience. A practical test: Put your website content into a Performance Max campaign builder and review how the system attempts to position your business. If you agree with the messaging, imagery, and framing, your site is likely easy to understand. If not, that feedback is valuable. You can also paste your site content into AI assistants and ask them to describe your business and services. If the response aligns with reality, you’re in a good place. If it doesn’t, that’s a signal to refine your content. Behavioral analytics tools, like Clarity, can help you understand exactly how humans are engaging with your site and how often AI tools are crawling your site. Dig deeper: AI tools for PPC, AI search, and social campaigns: What’s worth using now 3. Budget across the entire funnel Lead gen has always struggled with long conversion cycles. That challenge doesn’t go away, and in some ways, it becomes more pronounced. AI-driven systems increasingly weigh sentiment, visibility, and contextual signals, not just last-click performance. If all of your budget and reporting focuses on immediate traffic, you may miss meaningful impact higher in the funnel. That means: Budgeting intentionally across awareness, consideration, and conversion. Applying the right metrics at each stage. Looking beyond traffic as the primary success indicator. In many lead gen models, citations, qualified leads, and eventual revenue tell a more accurate story than clicks alone. Dig deeper: Lead gen PPC: How to optimize for conversions and drive results Get the newsletter search marketers rely on. See terms. 4. Clean up your feeds and map data You may not think you have a “feed” in your lead gen setup, but that absence can put you at a disadvantage. Feeds help AI systems understand your business structure, services, and site architecture. Even if you don’t have hundreds of pages, a simple, well-maintained feed in an Excel document can provide valuable context when uploaded to ad platforms. Example of a feed for lead gen Feed hygiene matters. Use clear, specific columns. Follow platform standards for text, images, and categorization. Make sure all relevant categories are represented. On the local side, claim and maintain all map profiles. Ensure information is accurate and consistent. If you use call tracking in map placements, review your labeling carefully. AI systems may pull data from map listings or your website, and mismatches can create attribution confusion, particularly for phone leads. Account for potential AI-driven inflation in reporting, whether you’re looking at map pack data, direct reporting, or site-level performance. Any changes you make should also be reflected correctly in your conversion goals. 5. Pressure-test your creative for clarity Creative assets may be mixed, matched, or shortened using AI. In some cases, you may only get one headline to explain who you are and why someone should contact you. If your value proposition requires three headlines, or a headline plus a description, to make sense, that’s a risk. Review your existing creative and identify assets that stand on their own. You should have at least some options where a single headline clearly communicates: What you do Who you help Why it matters If that clarity isn’t there, AI-driven placements can quickly become confusing. Dig deeper: Why creative, not bidding, is limiting PPC performance The fundamentals that still move the needle Lead gen today doesn’t need to be complicated. Most of the actions that matter today are things strong advertisers already do: clean data, clear messaging, intentional budgeting, and disciplined execution. What changes is how attribution may shift, and how much weight systems place on different signals. The fundamentals still win. The difference is that AI makes weaknesses more visible and strengths more scalable. If you focus on clarity, accuracy, and alignment across your funnel, you give both people and systems the best possible chance to understand your business — and that’s where sustainable performance comes from. View the full article

Five years ago, while working at Apple as a product designer, Mary Ann Rau decided to electrify her house and move away from fossil fuels. She installed solar, a battery, an induction range, and owned an EV. But there was still one big challenge: her HVAC system. “When it came to heat pumps, I was shocked when I got a quote for $40,000 to install heat pumps in my own house,” Rau says. Today, Rau launched a startup that’s tackling the problem of making heat pumps more accessible. Merino Energy, which just came out of stealth, makes heat pumps that each take an hour or less to install and come with a fixed price per unit of $3,800, including installation fees. For a whole home, the system could be half the cost of a typical mini split installation. (For someone who wants to add a unit to a single room, it’s an even bigger difference, coming in at a third of the cost.) Mary Ann RauBrad Hall Rau left Apple in 2023 and first worked at Quilt, another startup designing sleek mini-split heat pumps. There, she learned about the complexity of installation, a major driver of cost. “I learned that that was really the true bottleneck when it comes to heat pump adoption,” she says. Mini split heat pumps—units that are installed in walls in rooms throughout a house, and then connected to a condenser outside—can each take eight hours to install. Skilled technicians have to add refrigerant to long lines between the mini splits and the condenser. Labor for the whole process is expensive. New window heat pumps are faster to install, but aren’t compatible with some windows. (Rau’s cofounder, Brad Hall, previously worked at Gradient, one of the companies pioneering window heat pumps, but couldn’t install the product at his own home.) They saw an opportunity to design a different alternative. Merino’s design eliminates outdoor units and the need for complicated connections. Instead, it uses two vents in the wall, one to pull air in and one for exhaust. Each unit takes around an hour to install, shrinking the overall cost. Because refrigerant is added at the factory and not on site, that also reduces labor. Adding refrigerant at the factory also means there’s little risk of it leaking; small leaks can be a major source of emissions. “One leaky mini split can erase the benefits of transitioning around 50 homes off of fossil fuels,” Rau says. The team worked closely with installers to understand what needed to change. Part of the challenge with existing heat pumps wasn’t just the installation time, but the fact that HVAC companies were spending valuable hours visiting homes to give quotes that homeowners ultimately couldn’t afford. By offering flat rate pricing, homeowners can decide beforehand if they want to move forward. The approach means that Merino can give installers better margins while still saving homeowners money. The team also carefully considered the product design. “It’s designed to blend into your home like a modern artifact,” says Rau, who worked on AirPod design at Apple. The heat pump is quiet as it runs, and relatively sleek at 7.8 inches deep, “so it doesn’t take up too much space visually in the room,” she says. It also integrates with smart home tech like Apple HomeKit and Google Home, as well as wearables like the Oura Ring. “There are clinical studies that show that you can improve sleep quality and sleep efficiency by reducing temperature by several degrees when you’re in REM,” Rau says. “So if you choose to integrate your Oura Ring with our product, then you will get the benefit of automatic temperature control based off of your sleep cycle.” The company is launching first in California, where there’s strong demand for heat pumps. Los Angeles recently passed a rule requiring landlords to keep indoor temperatures below 82 degrees as extreme heat becomes more common. Many apartments in L.A. still lack air conditioning, making the new tech a relatively affordable way to add it permanently. Merino also makes a simplified version for low-income housing and recently installed it in a Bay Area apartment building for formerly homeless residents The state also has an aggressive goal to install six million heat pumps by 2030. “At the current pace of installation, it would take until 2045 for California to hit its goals,” Rau says. “With Merino, we think that we can actually do it in four years, and that’s because we’re doing installation eight times faster.” The startup opened preorders on its website today, and expects to ship the first products in late 2026. View the full article

A small business loan calculator is a practical tool that helps you estimate your monthly loan payments by inputting key factors like loan amount, interest rate, and loan term. It simplifies the process of comprehending your financial commitments and potential costs associated with borrowing. By using this calculator, you can make informed decisions about financing options. But what specific variables should you consider, and how can they impact your overall loan experience? Key Takeaways A small business loan calculator estimates monthly payments based on loan amount, interest rate, and term duration. It helps users understand total interest paid and overall loan cost over time. Users input desired loan amount, interest rate, and term to generate payment estimates. The calculator can also provide amortization schedules, detailing payment breakdowns throughout the loan’s life. It’s essential to verify estimates with lenders, as results are approximations influenced by input accuracy. What Is a Small Business Loan Calculator? A small business loan calculator is an essential online tool that helps you estimate your monthly payments based on significant factors like the loan amount, term, and interest rate or APR. This calculator allows you to input key financial figures, giving you a detailed estimate of monthly payments, total interest paid, and the overall cost of the loan. Many small business loan calculators additionally generate amortization schedules, breaking down how each payment impacts the principal and interest over time. By utilizing this tool, you can assess your borrowing capacity, typically between 10-30% of your annual revenue, considering your income, expenses, and credit score. How Does a Small Business Loan Calculator Work? How does a small business loan calculator provide valuable insights into your financing options? This online tool allows you to input your desired loan amount, interest rate, and loan term. By doing so, you can estimate monthly payments and the total interest paid over the loan’s life. The commercial loan payment calculator can likewise generate an amortization schedule, showing how each payment is split between principal and interest. You can adjust various factors, like loan amount and repayment term, to see how different scenarios affect your monthly payments. Moreover, it considers your annual revenue and expenses, helping you assess your borrowing capacity. Keep in mind, these results are estimates and should be verified with lenders for accuracy. Benefits of Using a Business Loan Calculator Using a small business loan calculator offers numerous benefits that can greatly aid in your financial planning. This tool helps you estimate monthly payments based on the loan amount, term, and APR, giving you a clear comprehension of your financial commitments. You can explore various loan scenarios by adjusting inputs, providing flexibility in your planning. Benefit Description Cost Insights Calculates total interest paid and total payments, revealing long-term costs. Amortization Schedule Some calculators offer a schedule detailing payment distribution over time. Affordability Evaluation Assists in ensuring your monthly cash flow can comfortably cover estimated payments. Key Variables to Input in the Calculator When using a small business loan calculator, you’ll need to input several key variables to get accurate results. Start with the loan amount, which is the total funds you’re looking to borrow, and then select the interest rate that reflects the cost of borrowing. Finally, specify the loan term duration, as this will determine how long you’ll have to repay the loan and influence your monthly payments. Loan Amount Input Determining the loan amount is a significant step in using a small business loan calculator, as this figure represents the total funds you intend to borrow for your business needs. Inputting the correct loan amount directly influences your estimated monthly payment. Typically, higher loan amounts result in higher payments. Most calculators let you input a range of amounts, helping you evaluate different borrowing scenarios and their financial impact. Here’s a quick overview of how loan amounts can affect your calculations: Loan Amount Estimated Monthly Payment Total Interest Paid $5,000 $100 $300 $10,000 $200 $600 $20,000 $400 $1,200 $30,000 $600 $1,800 $50,000 $1,000 $3,000 Understanding the loan amount is essential for evaluating affordability and comparing various small business loan rates. Interest Rate Selection How do you select the right interest rate for your small business loan? When using a commercial loan calculator, you can input the interest rate as either the APR (Annual Percentage Rate) or a simple interest rate, depending on what’s available. If you’re unsure of the interest rate, you can convert factor rates using specific formulas for a more accurate estimate. Remember, the interest rate you choose will greatly affect your monthly payments; lower rates lead to smaller payments and less total interest paid. Furthermore, be mindful of any fees associated with the loan, as these can impact the true cost and should be included in the APR for a thorough calculation. Adjusting the interest rate in the calculator allows you to evaluate different loan scenarios. Loan Term Duration Selecting the right loan term duration is crucial for managing your small business loan effectively, as it directly influences your monthly payment amount and the total interest paid over the life of the loan. Loan terms typically range from a few months to several years. Longer terms can lower your monthly payments, but they often result in higher total interest costs. By using a commercial loan amortization calculator, you can input different loan term durations to see how they affect both your monthly payments and overall interest. Furthermore, some calculators allow you to select various payment frequencies, which can further impact your repayment structure. Comprehending these factors helps you make informed decisions regarding your financing needs. Understanding Monthly Payments and Total Costs When you’re looking to finance your small business, comprehension of monthly payments and total costs is vital for effective budgeting. A business loan repayment calculator helps you estimate monthly payments by factoring in the loan amount, interest rate, and loan term. Monthly payments typically cover principal, interest, and fees, providing a full picture of your obligations. You can additionally view the total interest paid over the loan term, making it easier to compare options. Loan Amount Interest Rate Monthly Payment $10,000 5% $188 $20,000 7% $402 $30,000 6% $580 $40,000 4% $888 $50,000 5% $1,060 Types of Business Loans You Can Calculate Comprehending the various types of business loans available can greatly ease your financing expedition. One option is an SBA loan, which is partially guaranteed by the Small Business Administration, offering lower interest rates and longer repayment terms for eligible borrowers. If you need larger financing, consider conventional loans, though they often come with higher interest rates and require a solid credit history. Equipment financing is another route, allowing you to purchase equipment with the equipment itself as collateral. Lines of credit provide flexible access to funds, letting you borrow only what you need. Finally, commercial real estate loans are customized for purchasing or renovating properties, usually requiring a larger down payment, making the sba loan calculator a useful tool for evaluating these options. How to Use a Business Loan Calculator Effectively Using a business loan calculator can greatly streamline your financing process, as it allows you to gain insights into your potential loan obligations. To use a business loan calculator SBA effectively, start by inputting the loan amount, term in months, and the annual percentage rate (APR). This will help you estimate your monthly payments and total interest paid over the loan’s duration. Experiment with different loan amounts and terms to find the best balance for your budget. If you’re unsure of the APR, leverage the option to convert factor rates into interest rates for accurate calculations. Finally, review the amortization schedule to see how payments break down over time, and remember to factor in additional fees that may impact your overall loan cost. Alternative Financing Options for Small Businesses Finding the right financing option can be a game-changer for your small business, especially if traditional loans aren’t on the table. Here are some alternative financing options to evaluate: Financing Option Description Pros/Cons Business Grants Free funding but hard to obtain because of strict applications. Pros: No repayment; Cons: Competitive. Personal Business Loans Higher interest rates, suitable for newer businesses. Pros: Accessible; Cons: Costly. Business Credit Cards Revolving capital, potential rewards on purchases. Pros: Flexible; Cons: High interest. Invoice Factoring Sell receivables for immediate cash. Pros: Quick cash; Cons: Fees involved. Invoice Financing Use invoices as collateral for loans. Pros: Fast access; Cons: Debt risk. Utilizing a business loan estimator can help you evaluate these options effectively. Common Mistakes to Avoid When Using the Calculator When you’re calculating potential loan costs, it’s crucial to avoid common pitfalls that can lead to inaccurate results. Using a commercial mortgage loan calculator can be helpful, but mistakes can skew your comprehension. Here are three mistakes to watch for: Entering Inaccurate Amounts or Terms: Make sure the loan amounts and terms you input are correct to avoid misleading estimates. Ignoring Additional Fees: Don’t forget to include origination or documentation fees, as these can greatly affect your total APR. Not Adjusting Interest Rates: Always check if the calculator requires you to input an interest rate or APR; overlooking this can distort your results. Resources for Further Assistance After addressing common mistakes with loan calculators, it’s important to know where to turn for further support as you navigate financing options. The Small Business Administration (SBA) website is a valuable resource for exploring funding programs and counseling customized to your needs. Local chambers of commerce can connect you with lenders and provide information on financing options. Moreover, various grant programs might be available locally, though they can be competitive. Online platforms like Lendio and LendingTree offer tools, including a commercial real estate loan calculator, to help you compare different financing options. Finally, consider working with a business loan broker who can leverage their expertise to find the best financing solutions for your situation. Evaluating Your Loan Options When you’re evaluating your loan options, it’s crucial to compare different loan types to find the best fit for your business needs. Comprehending how payment structures work will help you anticipate your monthly obligations, whereas evaluating total loan costs guarantees you grasp the overall financial impact. Comparing Loan Types Choosing the right type of loan for your small business involves careful consideration of various options, as each type has its unique features and implications. Here are three key types to compare: SBA Loans: Often have lower interest rates because of government backing, making them a cost-effective option. Equipment Financing: Provides 100% funding particularly for necessary equipment, helping you acquire assets without upfront costs. Lines of Credit: Offer flexible access to funds, with interest only on the amount you use, which can be beneficial for managing cash flow. Using a business mortgage calculator can help you evaluate the total cost of each option, including interest and fees, ensuring you make an informed decision that aligns with your financial goals. Understanding Payment Structures Grasping the payment structure of your small business loan is crucial, as it directly impacts your budgeting and cash flow management. A small business loan calculator, like a business property loan calculator, helps you estimate monthly payments based on factors such as loan amount, interest rate, and loan term. Comprehending that monthly payments typically include principal, interest, and any applicable fees gives you an all-encompassing view of your loan’s cost. You can likewise calculate the total interest paid over the loan’s life, which helps clarify the overall expense. Generating an amortization schedule shows how each payment affects both principal and interest over time. Don’t forget to explore early repayment options, as they may save you money on total interest if no prepayment penalties apply. Assessing Total Loan Costs How can you accurately assess the total costs associated with a small business loan? Using a commercial building loan calculator can help you break down your financial obligations effectively. Here’s what you should consider: Loan Amount and Term: Enter the total amount you need and the desired repayment period. Annual Percentage Rate (APR): Input the interest rate to see how it impacts your payments. Additional Fees: Don’t forget to factor in origination and documentation fees, as these affect the true cost of borrowing. Tips for Managing Business Loans Managing business loans effectively is crucial for maintaining your financial health, especially as your business grows and evolves. Regularly review your loan payments and cash flow to guarantee you can cover monthly obligations comfortably. Setting up automatic payments can help you avoid late fees, adding unnecessary costs. Prioritize paying down high-interest loans first to minimize total interest paid over time. If interest rates drop or your credit improves, consider refinancing to lower your monthly payments. Furthermore, maintain open communication with your lender, especially during financial hardships, to explore options for payment adjustments or deferments. Using an SBA loan repayment calculator can help you plan and manage your loan obligations efficiently, making certain you stay on track. Frequently Asked Questions How Much Is a $50,000 Business Loan Monthly? For a $50,000 business loan, your monthly payment depends on the loan term and interest rate. For example, at a 15% interest rate over five years, you’d pay about $1,266.76 monthly. If you choose a longer term, your payments may decrease, but the total interest paid will increase considerably. Moreover, keep in mind any origination fees or other charges, as these can raise your total borrowing costs beyond just the interest. How Does Getting a Small Business Loan Work? Getting a small business loan involves several steps. First, you’ll submit a loan application with required documents like tax returns and financial statements. Lenders evaluate your eligibility based on your credit score, revenue, and debt service coverage ratio. If approved, you receive the loan amount and must repay it regularly, either monthly, weekly, or daily. How Much Can I Realistically Get for a Small Business Loan? To determine how much you can realistically get for a small business loan, consider factors like your annual revenue, credit score, and time in operation. Lenders usually lend 10% to 30% of your revenue. A strong credit score improves your chances of better terms. Furthermore, a debt service coverage ratio of 1.25 or higher is often required for approval. Evaluating these elements will give you a clearer picture of your borrowing potential. What Is the Payment on a $1,000,000 Business Loan? If you take out a $1,000,000 business loan at a 5% APR over 10 years, your monthly payment would be about $10,607. This arrangement leads to a total repayment of roughly $1,276,800, including around $276,800 in interest. Opting for a 15-year term lowers your monthly payment to about $7,908, but increases total interest to approximately $480,000. Remember to factor in any fees or early repayment options affecting your overall costs. Conclusion In conclusion, a small business loan calculator is a valuable tool that helps you grasp your potential loan obligations. By entering key variables like loan amount, interest rate, and term length, you can estimate monthly payments and total costs. This insight allows you to evaluate different financing options and make informed decisions. Remember to input accurate data and consider various scenarios, as this will improve your comprehension of your financial commitments and refine your loan management strategy. Image via Google Gemini This article, "What Is a Small Business Loan Calculator and How Does It Work?" was first published on Small Business Trends View the full article

A small business loan calculator is a practical tool that helps you estimate your monthly loan payments by inputting key factors like loan amount, interest rate, and loan term. It simplifies the process of comprehending your financial commitments and potential costs associated with borrowing. By using this calculator, you can make informed decisions about financing options. But what specific variables should you consider, and how can they impact your overall loan experience? Key Takeaways A small business loan calculator estimates monthly payments based on loan amount, interest rate, and term duration. It helps users understand total interest paid and overall loan cost over time. Users input desired loan amount, interest rate, and term to generate payment estimates. The calculator can also provide amortization schedules, detailing payment breakdowns throughout the loan’s life. It’s essential to verify estimates with lenders, as results are approximations influenced by input accuracy. What Is a Small Business Loan Calculator? A small business loan calculator is an essential online tool that helps you estimate your monthly payments based on significant factors like the loan amount, term, and interest rate or APR. This calculator allows you to input key financial figures, giving you a detailed estimate of monthly payments, total interest paid, and the overall cost of the loan. Many small business loan calculators additionally generate amortization schedules, breaking down how each payment impacts the principal and interest over time. By utilizing this tool, you can assess your borrowing capacity, typically between 10-30% of your annual revenue, considering your income, expenses, and credit score. How Does a Small Business Loan Calculator Work? How does a small business loan calculator provide valuable insights into your financing options? This online tool allows you to input your desired loan amount, interest rate, and loan term. By doing so, you can estimate monthly payments and the total interest paid over the loan’s life. The commercial loan payment calculator can likewise generate an amortization schedule, showing how each payment is split between principal and interest. You can adjust various factors, like loan amount and repayment term, to see how different scenarios affect your monthly payments. Moreover, it considers your annual revenue and expenses, helping you assess your borrowing capacity. Keep in mind, these results are estimates and should be verified with lenders for accuracy. Benefits of Using a Business Loan Calculator Using a small business loan calculator offers numerous benefits that can greatly aid in your financial planning. This tool helps you estimate monthly payments based on the loan amount, term, and APR, giving you a clear comprehension of your financial commitments. You can explore various loan scenarios by adjusting inputs, providing flexibility in your planning. Benefit Description Cost Insights Calculates total interest paid and total payments, revealing long-term costs. Amortization Schedule Some calculators offer a schedule detailing payment distribution over time. Affordability Evaluation Assists in ensuring your monthly cash flow can comfortably cover estimated payments. Key Variables to Input in the Calculator When using a small business loan calculator, you’ll need to input several key variables to get accurate results. Start with the loan amount, which is the total funds you’re looking to borrow, and then select the interest rate that reflects the cost of borrowing. Finally, specify the loan term duration, as this will determine how long you’ll have to repay the loan and influence your monthly payments. Loan Amount Input Determining the loan amount is a significant step in using a small business loan calculator, as this figure represents the total funds you intend to borrow for your business needs. Inputting the correct loan amount directly influences your estimated monthly payment. Typically, higher loan amounts result in higher payments. Most calculators let you input a range of amounts, helping you evaluate different borrowing scenarios and their financial impact. Here’s a quick overview of how loan amounts can affect your calculations: Loan Amount Estimated Monthly Payment Total Interest Paid $5,000 $100 $300 $10,000 $200 $600 $20,000 $400 $1,200 $30,000 $600 $1,800 $50,000 $1,000 $3,000 Understanding the loan amount is essential for evaluating affordability and comparing various small business loan rates. Interest Rate Selection How do you select the right interest rate for your small business loan? When using a commercial loan calculator, you can input the interest rate as either the APR (Annual Percentage Rate) or a simple interest rate, depending on what’s available. If you’re unsure of the interest rate, you can convert factor rates using specific formulas for a more accurate estimate. Remember, the interest rate you choose will greatly affect your monthly payments; lower rates lead to smaller payments and less total interest paid. Furthermore, be mindful of any fees associated with the loan, as these can impact the true cost and should be included in the APR for a thorough calculation. Adjusting the interest rate in the calculator allows you to evaluate different loan scenarios. Loan Term Duration Selecting the right loan term duration is crucial for managing your small business loan effectively, as it directly influences your monthly payment amount and the total interest paid over the life of the loan. Loan terms typically range from a few months to several years. Longer terms can lower your monthly payments, but they often result in higher total interest costs. By using a commercial loan amortization calculator, you can input different loan term durations to see how they affect both your monthly payments and overall interest. Furthermore, some calculators allow you to select various payment frequencies, which can further impact your repayment structure. Comprehending these factors helps you make informed decisions regarding your financing needs. Understanding Monthly Payments and Total Costs When you’re looking to finance your small business, comprehension of monthly payments and total costs is vital for effective budgeting. A business loan repayment calculator helps you estimate monthly payments by factoring in the loan amount, interest rate, and loan term. Monthly payments typically cover principal, interest, and fees, providing a full picture of your obligations. You can additionally view the total interest paid over the loan term, making it easier to compare options. Loan Amount Interest Rate Monthly Payment $10,000 5% $188 $20,000 7% $402 $30,000 6% $580 $40,000 4% $888 $50,000 5% $1,060 Types of Business Loans You Can Calculate Comprehending the various types of business loans available can greatly ease your financing expedition. One option is an SBA loan, which is partially guaranteed by the Small Business Administration, offering lower interest rates and longer repayment terms for eligible borrowers. If you need larger financing, consider conventional loans, though they often come with higher interest rates and require a solid credit history. Equipment financing is another route, allowing you to purchase equipment with the equipment itself as collateral. Lines of credit provide flexible access to funds, letting you borrow only what you need. Finally, commercial real estate loans are customized for purchasing or renovating properties, usually requiring a larger down payment, making the sba loan calculator a useful tool for evaluating these options. How to Use a Business Loan Calculator Effectively Using a business loan calculator can greatly streamline your financing process, as it allows you to gain insights into your potential loan obligations. To use a business loan calculator SBA effectively, start by inputting the loan amount, term in months, and the annual percentage rate (APR). This will help you estimate your monthly payments and total interest paid over the loan’s duration. Experiment with different loan amounts and terms to find the best balance for your budget. If you’re unsure of the APR, leverage the option to convert factor rates into interest rates for accurate calculations. Finally, review the amortization schedule to see how payments break down over time, and remember to factor in additional fees that may impact your overall loan cost. Alternative Financing Options for Small Businesses Finding the right financing option can be a game-changer for your small business, especially if traditional loans aren’t on the table. Here are some alternative financing options to evaluate: Financing Option Description Pros/Cons Business Grants Free funding but hard to obtain because of strict applications. Pros: No repayment; Cons: Competitive. Personal Business Loans Higher interest rates, suitable for newer businesses. Pros: Accessible; Cons: Costly. Business Credit Cards Revolving capital, potential rewards on purchases. Pros: Flexible; Cons: High interest. Invoice Factoring Sell receivables for immediate cash. Pros: Quick cash; Cons: Fees involved. Invoice Financing Use invoices as collateral for loans. Pros: Fast access; Cons: Debt risk. Utilizing a business loan estimator can help you evaluate these options effectively. Common Mistakes to Avoid When Using the Calculator When you’re calculating potential loan costs, it’s crucial to avoid common pitfalls that can lead to inaccurate results. Using a commercial mortgage loan calculator can be helpful, but mistakes can skew your comprehension. Here are three mistakes to watch for: Entering Inaccurate Amounts or Terms: Make sure the loan amounts and terms you input are correct to avoid misleading estimates. Ignoring Additional Fees: Don’t forget to include origination or documentation fees, as these can greatly affect your total APR. Not Adjusting Interest Rates: Always check if the calculator requires you to input an interest rate or APR; overlooking this can distort your results. Resources for Further Assistance After addressing common mistakes with loan calculators, it’s important to know where to turn for further support as you navigate financing options. The Small Business Administration (SBA) website is a valuable resource for exploring funding programs and counseling customized to your needs. Local chambers of commerce can connect you with lenders and provide information on financing options. Moreover, various grant programs might be available locally, though they can be competitive. Online platforms like Lendio and LendingTree offer tools, including a commercial real estate loan calculator, to help you compare different financing options. Finally, consider working with a business loan broker who can leverage their expertise to find the best financing solutions for your situation. Evaluating Your Loan Options When you’re evaluating your loan options, it’s crucial to compare different loan types to find the best fit for your business needs. Comprehending how payment structures work will help you anticipate your monthly obligations, whereas evaluating total loan costs guarantees you grasp the overall financial impact. Comparing Loan Types Choosing the right type of loan for your small business involves careful consideration of various options, as each type has its unique features and implications. Here are three key types to compare: SBA Loans: Often have lower interest rates because of government backing, making them a cost-effective option. Equipment Financing: Provides 100% funding particularly for necessary equipment, helping you acquire assets without upfront costs. Lines of Credit: Offer flexible access to funds, with interest only on the amount you use, which can be beneficial for managing cash flow. Using a business mortgage calculator can help you evaluate the total cost of each option, including interest and fees, ensuring you make an informed decision that aligns with your financial goals. Understanding Payment Structures Grasping the payment structure of your small business loan is crucial, as it directly impacts your budgeting and cash flow management. A small business loan calculator, like a business property loan calculator, helps you estimate monthly payments based on factors such as loan amount, interest rate, and loan term. Comprehending that monthly payments typically include principal, interest, and any applicable fees gives you an all-encompassing view of your loan’s cost. You can likewise calculate the total interest paid over the loan’s life, which helps clarify the overall expense. Generating an amortization schedule shows how each payment affects both principal and interest over time. Don’t forget to explore early repayment options, as they may save you money on total interest if no prepayment penalties apply. Assessing Total Loan Costs How can you accurately assess the total costs associated with a small business loan? Using a commercial building loan calculator can help you break down your financial obligations effectively. Here’s what you should consider: Loan Amount and Term: Enter the total amount you need and the desired repayment period. Annual Percentage Rate (APR): Input the interest rate to see how it impacts your payments. Additional Fees: Don’t forget to factor in origination and documentation fees, as these affect the true cost of borrowing. Tips for Managing Business Loans Managing business loans effectively is crucial for maintaining your financial health, especially as your business grows and evolves. Regularly review your loan payments and cash flow to guarantee you can cover monthly obligations comfortably. Setting up automatic payments can help you avoid late fees, adding unnecessary costs. Prioritize paying down high-interest loans first to minimize total interest paid over time. If interest rates drop or your credit improves, consider refinancing to lower your monthly payments. Furthermore, maintain open communication with your lender, especially during financial hardships, to explore options for payment adjustments or deferments. Using an SBA loan repayment calculator can help you plan and manage your loan obligations efficiently, making certain you stay on track. Frequently Asked Questions How Much Is a $50,000 Business Loan Monthly? For a $50,000 business loan, your monthly payment depends on the loan term and interest rate. For example, at a 15% interest rate over five years, you’d pay about $1,266.76 monthly. If you choose a longer term, your payments may decrease, but the total interest paid will increase considerably. Moreover, keep in mind any origination fees or other charges, as these can raise your total borrowing costs beyond just the interest. How Does Getting a Small Business Loan Work? Getting a small business loan involves several steps. First, you’ll submit a loan application with required documents like tax returns and financial statements. Lenders evaluate your eligibility based on your credit score, revenue, and debt service coverage ratio. If approved, you receive the loan amount and must repay it regularly, either monthly, weekly, or daily. How Much Can I Realistically Get for a Small Business Loan? To determine how much you can realistically get for a small business loan, consider factors like your annual revenue, credit score, and time in operation. Lenders usually lend 10% to 30% of your revenue. A strong credit score improves your chances of better terms. Furthermore, a debt service coverage ratio of 1.25 or higher is often required for approval. Evaluating these elements will give you a clearer picture of your borrowing potential. What Is the Payment on a $1,000,000 Business Loan? If you take out a $1,000,000 business loan at a 5% APR over 10 years, your monthly payment would be about $10,607. This arrangement leads to a total repayment of roughly $1,276,800, including around $276,800 in interest. Opting for a 15-year term lowers your monthly payment to about $7,908, but increases total interest to approximately $480,000. Remember to factor in any fees or early repayment options affecting your overall costs. Conclusion In conclusion, a small business loan calculator is a valuable tool that helps you grasp your potential loan obligations. By entering key variables like loan amount, interest rate, and term length, you can estimate monthly payments and total costs. This insight allows you to evaluate different financing options and make informed decisions. Remember to input accurate data and consider various scenarios, as this will improve your comprehension of your financial commitments and refine your loan management strategy. Image via Google Gemini This article, "What Is a Small Business Loan Calculator and How Does It Work?" was first published on Small Business Trends View the full article

New research shows AI Mode is reshaping buying decisions. Learn how to secure visibility, trust, and top placement. The post How Consumers Navigate High-Stakes Purchases In AI Mode appeared first on Search Engine Journal. View the full article

After traveling deeper into space than any other humans, the Artemis II astronauts pointed their moonship toward home Monday night, wrapping up a lunar cruise that revealed views of the far side never beheld by eyes until now. Their flyby of the moon — NASA’s first return since the Apollo era — even included some celestial sightseeing besides yielding rich science. It was a significant step toward landing boot prints near the moon’s south pole in just two years. A total solar eclipse greeted the three Americans and one Canadian as the moon temporarily blocked the sun from their perspective. Mercury, Venus, Mars and Saturn nodded at them from the black void. The landing sites of Apollo 12 and 14 also were visible, poignant reminders of NASA’s first age of exploration more than half a century ago. In an especially riveting retro throwback, Artemis II shattered the distance record set by Apollo 13 in 1970. NASA’s Orion capsule reached a maximum distance of 252,756 miles (406,771 kilometers) from Earth before hanging a U-turn behind the moon, 4,101 miles (6,600 kilometers) farther than Apollo 13. “It is blowing my mind what you can see with the naked eye from the moon right now. It is just unbelievable,” Canadian astronaut Jeremy Hansen radioed. He challenged “this generation and the next to make sure this record is not long-lived.” Artemis II astronauts get an Apollo wake-up message Apollo 13 commander Jim Lovell wished the crew well in a recording made two months before his death last August. Mission Control beamed up his message to commander Reid Wiseman, pilot Victor Glover, Christina Koch and Hansen, before their fly-around began. “Welcome to my old neighborhood,” said Lovell, who also flew on Apollo 8, humanity’s first lunar visit. “It’s a historic day and I know how busy you’ll be, but don’t forget to enjoy the view.” The Artemis II astronauts carried up with them the Apollo 8 silk patch that accompanied Lovell to the moon. “It’s just a real honor to have that on board with us,” Wiseman said. Artemis II is using the same maneuver that Apollo 13 did after its “Houston, we’ve had a problem” oxygen tank explosion wiped out any hope of a moon landing. Known as a free-return lunar trajectory, this no-stopping-to-land route takes advantage of Earth and the moon’s gravity, reducing the need for fuel. It’s a celestial figure-eight that put the astronauts on course for home once they emerged from behind the moon Monday evening. Astronauts lock in on lunar observations Artemis II’s lunar fly-around and intense observation period lasted seven hours, by far the highlight of the nearly 10-day test flight that will end with a splashdown in the Pacific on Friday. Venturing as close as 4,067 miles (6,545 kilometers) to the gray dusty surface, the astronauts zipped through a list of more than two dozen targets, using powerful Nikon cameras as well as their iPhones to zoom in on impact craters and other intriguing lunar features. Before getting started, they requested permission to name two bright, freshly carved craters. They suggested Integrity, the name of their capsule, and Carroll, commander Wiseman’s wife, who died of cancer in 2020. Wiseman wept as Hansen put in the request to Mission Control, and all four astronauts embraced in tears. “Such a majestic view out here,” Wiseman radioed once he regained his composure and started picture-taking. The astronauts called down that they managed to capture the moon and Earth in the same shot, and they provided a running commentary to scientists back in Houston on what they were seeing. At one point, Koch reported an overwhelming sensation of emotion for a second or two while zooming in on the moon. “Something just drew me in suddenly to the lunar landscape and it became real,” she said. The Artemis II astronauts made their closest approach to the moon and reached their maximum distance from Earth while they were out of contact. Their speed at closest approach: 3,139 mph (5,052 kph). The spacecraft accelerated as it appeared from behind the moon and the planned communications blackout and made tracks for Earth. An Earthrise came into view showing Asia, Africa and Oceania as Mission Control called out: “We are Earthbound and ready to bring you home.” Flight controllers in Houston flipped their mission patches over to signify the return leg. President Donald The President phoned the astronauts following the flyby, calling them “modern-day pioneers.” “Today you’ve made history and made all America really proud, incredibly proud,” the president said, adding that more lunar traveling is coming and ultimately “the whole big trip to Mars.” Wiseman and his crew spent years studying lunar geography to prepare for the big event, adding solar eclipses to their repertoire during the past few weeks. By launching last Wednesday, they ensured themselves of a total solar eclipse from their vantage point behind the moon, courtesy of the cosmos. Topping their science target list: Orientale Basin, a sprawling impact basin with three concentric rings, the outermost of which stretches nearly 600 miles (950 kilometers) across. Their moon mentor, NASA geologist Kelsey Young, expects thousands of pictures. Artemis II is NASA’s first astronaut moonshot since Apollo 17 in 1972. It sets the stage for next year’s Artemis III, which will see another Orion crew practice docking with lunar landers in orbit around Earth. The culminating moon landing by two astronauts near the moon’s south pole will follow on Artemis IV in 2028. While Artemis II may be taking Apollo 13’s path, it’s most reminiscent of Apollo 8 and humanity’s first lunar visitors who orbited the moon on Christmas Eve 1968 and read from the Book of Genesis. Glover said flying to the moon during Christianity’s Holy Week brought home for him “the beauty of creation.” Earth is an oasis amid “a whole bunch of nothing, this thing we call the universe” where humanity exists as one, he observed over the weekend. “This is an opportunity for us to remember where we are, who we are, and that we are the same thing and that we’ve got to get through this together,” Glover said, clasping hands with his crewmates. The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Department of Science Education and the Robert Wood Johnson Foundation. The AP is solely responsible for all content. —Marcia Dunn, AP Aerospace Writer View the full article

The workplace has seen its share of technological shifts, but the rise of AI is happening at a much faster pace. What once took years is now unfolding in months, leaving little time for companies or their employees to catch up. A new global study of 2,400 employees and C-suite leaders conducted by Workplace Intelligence and enterprise AI agent platform WRITER finds that 60% of companies plan to lay off employees who won’t adopt AI. Even more striking, 77% of executives say those who resist AI won’t be considered for promotions or leadership roles. AI isn’t just another tool. It’s quickly becoming a baseline expectation for staying relevant at work. This shift is already reshaping how companies evaluate talent. According to the research, 92% of executives say they are actively cultivating a new class of “AI elite” employees, and 87% report that these employees are at least five times more productive than their peers. That productivity gap is creating a two-tier workforce: those who know how to leverage AI to amplify their output, and those who don’t. The implications for career growth are profound. The most valuable employees are no longer just high performers, but those who can combine domain expertise with AI fluency to move faster and scale their impact. Companies aren’t just talking about this shift—they’re enforcing it. At Accenture, senior staff who fail to use AI tools risk missing out on promotions, signaling that AI proficiency is becoming a prerequisite for advancement. Meanwhile, tech giants like Meta are embedding AI usage into daily workflows and performance expectations. Some organizations are also using incentives to accelerate behavior change. KPMG, for instance, has offered financial rewards to employees who develop innovative AI use cases, reinforcing that those who embrace the technology will be recognized. Yet for all the momentum, the reality inside organizations is far more complicated. While 97% of executives say AI has been beneficial, only a minority report seeing significant returns from generative AI (29%) or AI agents (23%). Nearly half of leaders say their AI adoption efforts have been a disappointment so far. This gap between expectation and outcome is fueling pressure at the highest levels. In fact, 38% of CEOs report a high or crippling amount of stress related to AI strategy, and 64% fear they could lose their job if they fail to lead their organizations through the transition. Part of the challenge is that many companies are building the plane while flying it. Despite widespread investment, 39% of executives admit they don’t have a formal strategy in place to drive revenue from AI, and 75% say their existing strategy is more for show than for actual guidance. The result is confusion and fragmentation. More than half of executives say AI adoption is creating internal power struggles, while 78% report tension between IT and other business units. In many organizations, AI usage has become fragmented, with employees experimenting in silos rather than working toward a unified vision. That lack of alignment is also contributing to resistance from employees. The study found that 29% of workers admit to sabotaging their company’s AI strategy, whether by using unauthorized tools, inputting sensitive data into public systems, or refusing to engage. Among Gen Z employees, that number jumps to 44%. For leaders, this behavior represents a real risk. More than three-quarters of executives say employee resistance and misuse of AI poses a serious threat to their organization’s future, especially as 67% report experiencing a data leak or security breach tied to AI usage. Despite these challenges, some organizations are getting it right by treating AI adoption as a business transformation rather than a technology rollout. Marriott International, for example, has focused on aligning AI initiatives with measurable business outcomes, ensuring that investments are tied to growth and operational improvements rather than experimentation alone. The most successful approaches share a few common elements. They empower employees to experiment with AI tools while providing clear guardrails, reducing the risk of security issues. They invest in building AI fluency across the organization so more employees can contribute effectively. They establish governance frameworks for AI systems to ensure innovation doesn’t outpace oversight. And they approach change management as both a top-down and bottom-up effort, recognizing that adoption requires executive alignment and employee buy-in. The message for workers is becoming clear: adapting to AI is no longer a future concern—it’s a present-day requirement. As roles, responsibilities, and performance metrics evolve, employees who fail to integrate AI into their workflows risk falling behind in both productivity and relevance. Those who embrace it, however, have an opportunity to redefine their value and accelerate their careers. For leaders, the stakes are just as high. The organizations that succeed won’t be the ones that simply deploy AI tools, but the ones that rethink how work gets done. That means closing the gap between ambition and execution, turning experimentation into impact, and ensuring that the workforce evolves alongside the technology. Because in the emerging AI economy, the real competitive advantage isn’t just having access to AI, but knowing how to use it. 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. Bose’s QuietComfort Ultra headphones have been easy to recommend since their late 2023 launch, but the price held them back. That changes with this open-box deal. Right now, the Bose QuietComfort Ultra Headphones in sandstone are down to $242.49 on Woot, compared to about $329 for a new pair on Amazon. Price trackers show earlier dips closer to $279, so this undercuts previous lows. The catch is the “open box” label—these units may have been returned, tested, or repackaged (but they’re cleared to work like new). Shipping is free for Prime members, while others pay $6. The deal is expected to last six days or until stock runs out. Bose QuietComfort Ultra Headphones $242.49 at Woot $429.00 Save $186.51 Get Deal Get Deal $242.49 at Woot $429.00 Save $186.51 Bose has long focused on cutting down low-frequency noise, and these headphones do a good job muting things like traffic, airplane hum, or AC rumble. Mid-range noise gets reduced well, too, though sharper, high-pitched sounds can still come through, notes this PCMag review. That’s normal for ANC, but it’s worth noting if you expect total silence. Compared to older rivals like the Apple AirPods Max and the Sony WH-1000XM5, Bose still holds its ground. Newer models like Sony’s XM6 push ahead, but they also cost more. Outside of ANC, the transparency mode works reliably for letting in voices and street sounds, and the Bose app gives you a clean, easy EQ to tweak audio to your liking. As for its battery life, it's solid but not class-leading. You’ll get around 24 hours on a full charge, or closer to 18 hours with ANC turned on. That’s enough for long flights or a few days of regular use, though some competitors stretch further. Comfort remains a strong point, with a lightweight design that works well for long listening sessions. The main tradeoff here is the open-box condition. If you’re fine with that, this price makes the QuietComfort Ultra far easier to justify. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods Pro 3 Noise Cancelling Heart Rate Wireless Earbuds — $224.00 (List Price $249.00) Apple iPad 11" 128GB A16 WiFi Tablet (Blue, 2025) — $299.99 (List Price $349.00) Samsung Galaxy Tab A11+ 128GB Wi-Fi 11" Tablet (Gray) — $209.99 (List Price $249.99) Apple Watch Series 11 (GPS, 42mm, S/M Black Sport Band) — $329.00 (List Price $399.00) Sony WH-1000XM5 — $248.00 (List Price $399.99) Deals are selected by our commerce team View the full article

For most people, “Mad Men” means the TV show. But the phrase points to something more specific: Madison Avenue in the 1950s and ‘60s, when agencies grew brands through persuasion, positioning, and earned trust in a world of scarce media channels and powerful gatekeepers. If you wanted attention, you bought your way in, then made your product the obvious choice. When the internet arrived and Google made the chaos navigable, an entire industry was built on getting brands found. Search and SEO became one of the most commercially valuable disciplines in marketing. That model isn’t disappearing. But something new is taking shape on top of it — and most of the industry is still using the wrong language to describe what’s happening. AI is exposing everything SEO has neglected. Brands that win recommendations from AI systems won’t do so by publishing more content. They’ll win through positioning, persuasion, and corroborated proof. In other words, they’ll win the way Madison Avenue always did. SEO was never really about content One of the strangest things about the current industry conversation is how many people talk as if the job of SEO is to create content. It isn’t. Not for most businesses. If you’re a publisher, content is the product. Traffic is the commercial engine. But for most brands, content never did what people thought. Early on, people wrote content for customers, and it worked. Then it changed. Content became a keyword vehicle. “Get people to our site” replaced good marketing comms. Traffic became a proxy for exposure. It worked because search rewarded retrieval: type a query, get a page, get a click. All you needed to sell that model was the belief that any traffic was good traffic. That traffic somehow led to revenue that your agency could keep delivering. That model is now under serious pressure. Google and ChatGPT are increasingly taking the click. Every serious large language model is trying to satisfy informational intent before the user reaches the source. They aren’t trying to be better search engines. They’re trying to make search engines unnecessary — and that’s the entire point. There’s too much information on the web. People don’t want to open 10 tabs and read five near-identical blog posts to find a basic answer. They want the answer. The AI systems exist precisely to give it to them. So if informational retrieval gets absorbed into the interface, what remains? Marketing. That’s the part many SEOs are still not fully grappling with. Dig deeper: The three AI research modes redefining search – and why brand wins 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 From place to preference The cleanest way to understand this shift is through the “4 Ps” of marketing: product, price, place, and promotion. Traditional SEO has been, almost entirely, a place discipline. It’s been about getting your products, services, or information onto the digital shelf when people go looking. Keyword rankings are shelf position. Paid search is just a more expensive version of the same principle. In commercial search, you pay for premium placement in a digital aisle. That still matters enormously. Buyer-intent search remains valuable. Google hasn’t solved its commercial transition to a fully AI-led interface, and won’t overnight. Search is too important to Google’s revenue to disappear fast. But another layer is emerging above it, and this is the layer that most agencies aren’t yet equipped to compete on. As AI systems become the first interaction point for more users, the game shifts from being present to being preferred. Users don’t just search. They ask. They describe a problem. They want the best CRM for a mid-market SaaS company, the best estate agent in their area, the best sandwich shop near the office. And the system responds with recommendations. If classic SEO was about rankings, the next phase is about recommendations. If classic SEO was about digital placement, the next phase is about shaping preference. And recommendation, in practice, is advertising. Not a display banner. Not a 30-second TV spot. But advertising in the oldest and most commercially powerful sense: influencing the choice someone makes before they’ve even consciously made it. An AI-generated recommendation is an invisible ad unit. It doesn’t bill by impression. Why AI recommendations hit differently When an LLM recommends a brand, it can’t know with certainty what will work best. So it infers. It weighs signals: past success, prominence, reviews, case studies, corroborating sources, and repeated associations between a brand and a specific type of problem. Humans do something almost identical. Where performance is clearly bounded, we can identify a winner. We know who won the Oscar. We know which film topped the box office. But when performance isn’t obvious in advance, we rely on proxies. We ask friends, read reviews, and scan for authority. We use familiarity, logic, and social proof to estimate what is likely to be right. That’s exactly the territory AI recommendation is now entering — the consideration set problem. If I ask an LLM to find me a reliable accountant for a small business, I’m not asking it to retrieve a blog post. I’m asking it to build me a shortlist. Unlike traditional search, the recommendation layer is invisible to brands unless they test for it actively. You don’t see the prompt or the source chain. You don’t even know why one brand made the cut and another didn’t. But the commercial effect is real, possibly stronger than anything traditional search produced. If you’re in the recommendation set, you’re in the running. If you’re absent, you’ve lost the sale before the conversation started. Dig deeper: Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it Get the newsletter search marketers rely on. See terms. Your website is now an argument for preference The first practical consequence: your website can no longer function like a polite digital brochure. Despite being optimized for search, many commercial web pages simply: Introduce the company. Gesture vaguely at services. Bury differentiation under generic corporate language. Treat the page as an endpoint for a ranking rather than a persuasive asset. Still, they’re weak where it matters most: actual selling. In the Mad Men era of SEO, your landing pages and service pages need to function like sales pages, not in a cheesy direct-response way, but in the strategic sense that they must clearly answer four things: Who is this for? What problem does it solve? Why is it different? Why choose it over the alternatives? This comes down to positioning, which is key to GEO. If seven brands do broadly the same thing, the model needs distinctions. It needs enough clarity to say: this brand is best for X kind of buyer with Y kind of problem because it does Z better than everyone else. Your website copy must surface real performance attributes: the specific things you genuinely do better or more distinctively than competitors. Your pages must become machine-readable arguments for preference. Copywriting is back Actual commercial copywriting — not fluffy brand storytelling or word count for its own sake — identifies a target customer, sharpens the problem, articulates the value, and makes the offer easy to recommend. Good copy isn’t optional. Take a local sandwich shop. The old SEO conversation runs to “best sandwich near me,” local pack, and review acquisition. It’s useful, but limited. The GEO version starts with the shop’s actual performance attributes. Is it the speed? The handmade bread? The office catering? The locally sourced produce? Those claims must be clear on the website first. Then they need corroboration everywhere else: Reviews that mention the sourdough specifically. A local food blogger’s write-up. Inclusion in “best lunch spots” roundups. They’re specific, repeated, retrievable evidence of why this shop is the right recommendation for a particular type of customer. Scale that logic to a B2B software company, and the principle holds. Pages that clearly explain who the product is for, which problems it solves, and why it outperforms rivals. Then build mentions, customer reviews, and gain trade-press coverage — the body of evidence to support recommending you to buyers — and let the AI find it. That’s pretty much GEO in a nutshell. Keywords don’t disappear, but they lose their throne Keywords are a human workaround. Approximations of intent, built for a retrieval system that needed exact string matching. LLMs process fuller context, layered needs, and comparative requirements. They move from keyword matching toward problem understanding. Keyword research still matters for classic search, paid search, and buyer-intent pages. But the center of gravity shifts. Instead of asking only “what terms should we rank for?”, the better question is: what attributes make us the right recommendation for the buyer we actually want, and what evidence exists across the web to support that claim? The future of SEO is starting to look like the old agency model, as the work is increasingly promotional. Once your website clearly expresses your positioning, the challenge becomes promoting that position across the wider web through credible, repeated, relevant signals. Digital PR. Traditional PR. Expert commentary. Case studies. Reviews. Listicles. Awards. Trade press. Brand mentions. Conference speaking. Events. Creator coverage. Product comparisons. Original data studies that other people actually cite. These are the things you go after, create, and encourage. Sadly, many “AI visibility” conversations flatten this into nonsense. The goal isn’t merely to have content cited by AI. It’s to gather enough market evidence that AI systems repeatedly encounter your brand in the right contexts, with the right associations. The work stops being optimization and becomes maximization: building the largest possible volume of persuasive, corroborated, retrievable evidence that your brand is a sensible recommendation for a specific kind of buyer. That’s a fundamentally different model from anything the SEO industry has been selling. It’s promotional and strategic brand marketing. Dig deeper: How to design content that AI systems prefer and promote Where SEO still fits SEOs need to grow up. There’s still significant value in buyer-intent search, technical site architecture, entity clarity, internal linking, and structured data. SEOs are well placed to monitor recommendation environments, test prompts, and identify where visibility is being won or lost. But the identity crisis is real. Many agencies were built for a world of rankings, informational blogs, and monthly traffic graphs. They aren’t equipped to lead a world defined by positioning, copy, PR, brand evidence, and recommendation science. Tracking brand citations inside AI outputs isn’t a complete strategy. It’s a temporary metric. See the complete picture of your search visibility. Track, optimize, and win in Google and AI search from one platform. Start Free Trial Get started with The new agency model Winning agencies look like hybrid commercial strategy firms: part SEO, part copywriting, part PR, part brand strategy, part technical infrastructure. They know how to protect buyer-intent search revenue today while building the fame, clarity, and corroborated authority that earns recommendation tomorrow. This is the Mad Men model of SEO. Persuasion, positioning, and clear claims backed by public proof matter again. And the job is to become recommended by AI. View the full article

AI is transforming companies everywhere. While some research has shown that women are falling behind in terms of AI adoption, at the leadership level women are highly involved in guiding AI strategy. According to new research from Chief, a network for senior women leaders, in partnership with The Harris Poll, women leaders are playing a key role in carefully building AI frameworks. The research, which polled 1,768 male, female, and nonbinary leaders, found that, overwhelmingly, women are driving AI strategy with 80% playing active roles in how it’s being implemented into workflows. Nearly a third (31%) said they were involved in AI governance, ethics, and responsible implementation. Another 25% said they design how humans and AI will work together in the organization, and 24% said they create and build AI solutions. Still, while women seem to be ahead of the game in shaping AI strategy, they are prioritizing responsible and intentional adoption over speed. According to the study, 83% of women agreed with the statement: “Being cautious about AI adoption is a sign of good leadership, not resistance to technology.” Still, the vast majority (68%) said that their organization prioritizes “speed over sustainable workforce implementation.” There’s a good reason for proceeding with caution: 62% of women respondents said their organization doesn’t fully understand “what AI can and can’t do.” Three-quarters said they expect critical thinking to decline if implementation doesn’t happen carefully and 81% said “capable managers” will become a thing of the past if companies don’t invest in their human workforce now. Similarly, a staggering 87% said they’ve already witnessed the fallout of companies focusing too heavily on an “AI only” approach that left employees underutilized. Alison Moore, CEO of Chief, said that doesn’t mean women are “slowing down” when it comes to AI’s implementation. They’re simply “making sure the humans keeping pace with it don’t get left behind in the process.” In other words, while some are ready to go all in on AI, women are leaning into their own critical thinking around AI implementation, so that critical thinking doesn’t disappear. This approach offers hope for the future during a time when AI is responsible for 25% of job cuts, except women are still only 29% of the C-suite. View the full article