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A reader writes: About a year ago, I got prescribed a CPAP machine. Very important for, you know, supplying oxygen to my brain while I sleep, but one doozy of an adjustment period. It took me about a month to adjust to wearing it at night, and during that month I lowkey felt like I was dying. I was getting very little sleep, and that in small bursts. I was exhausted all the time, and exhaustion made me stupid and slow. I work in a compliance-related role. My job involves assessing regulatory liability for my employer and potential misconduct by licensed employees. If I find against an employee, it’s the kind of thing that could follow them for the rest of their career, whether at my firm or any other they move to. If I find in favor of my firm where I should have found fault, that can open us up to regulatory complaints and investigations. Operating on broken and insufficient sleep for a month while facing those potential consequences for bad calls scared the dickens out of me. I had productivity numbers to meet, but I simply could not stay focused enough to work at the normal speed, and awareness of the potential stakes of an error of judgment made me extra cautious. I was operating at about 40% of our expected performance, and even after I adjusted it took me some more time to fully get back up to speed as I paid off the sleep debt. But a month-plus of turning out a fraction of the work I’m expected to do had a predictably terrible effect on my career. I wound up on a performance improvement plan and lost a lot of credibility with my boss. And unfortunately for me, my boss is the kind of guy who doesn’t really understand exhaustion as an excuse. As he sees it, either you’re so badly off you should take PTO or you’re fine and coming in to work and doing what needs doing. But I couldn’t exactly take an entire month of PTO, that’s far more than my allotment! And I don’t think short-term disability can be applied here. I had a similar situation early in my career, too, when I was prescribed a strong bronchitis medication that interfered with my judgment and focus during the two weeks I was taking it. I only had five days’ sick time and had used half of it, so the only option I saw was to go to work high, which even at entry-level stakes is a bad idea. So, how does one navigate these situations? My understanding is that accommodations for health are meant to offer you support to maintain the expected productivity, not to make it okay to underperform. Are there ways to approach an “I know I’m underperforming but I can’t do better until my body stops doing a stupid thing, which is some indefinite number of weeks away” conversation that could actually sound credible? How do people navigate this? The wording you want is, “I’m dealing with a medical situation that is making it hard to be at 100% right now. I’m working with my doctor to resolve it and we’re hopeful I’ll be back to normal soon, but I wanted to mention it in case you notice me seeming off my usual game.” Or, “I want to let you know that I’m dealing with a medical condition that has been wearing me out lately. I’m working with my doctor on a treatment plan and I don’t expect it to continue long-term, but I wanted to mention it in case you notice me seeming off.” You don’t need to disclose details — just you might notice this, I’m working on it, and I’m hoping it will be resolved soon. It’s ideal to say it before your boss talks to you about changes in your work, but if you didn’t, you can still say it once they do. The idea is to give your manager context for what’s happening so they don’t have to wonder if you’re just being careless or aren’t invested in your job anymore, or otherwise draw the wrong conclusions about what’s going on. Most managers will give you a lot more slack if you explain that yes, you’ve flagged it too, there’s a reason for it, and you’re working to resolve it. The post how do I handle being off my game at work because of a medical situation? appeared first on Ask a Manager. View the full article

Prime minister says ‘things can’t go on like this’ in meeting with tech groups View the full article

As founder, chair, and CEO of the Exceptional Women Alliance, I am privileged to engage with extraordinary female leaders across industries. This month, I spoke with Shari Hofer about a workforce issue hiding in plain sight: eldercare. For many organizations, caregiving is still viewed as something employees manage quietly outside of work. According to the 2025 Caregiving in the US report, released by AARP and the National Alliance for Caregiving, approximately 63 million Americans provide family care, with almost 48 million providing unpaid care to adults. The 2021 AARP report estimated that the economic value of care was over $600 billion annually. Today, these professionals are simultaneously leading teams, raising families, and caring for aging parents. The result is an invisible workforce dynamic that is already impacting performance, retention, and leadership effectiveness. Hofer wrote With Love: A Practical Guide for Caregiving for Aging Parents Through End of Life with coauthor Shabnam Kazmi. Both built demanding global careers while raising families and caring for aging parents through dementia, Parkinson’s, and end-of-life decisions. Their new book reframes caregiving not as a private hardship, but as a leadership and organizational imperative. Q: How did your caregiving journey begin, and when did it significantly intensify—both personally and professionally? Hofer: It began when my siblings and I learned our father had macular degeneration and dementia. We had already lost our mother, and we were all living in different states. We knew the road ahead would be long and complex, and that we would need to step in, because our father wouldn’t make any changes or be able to care for himself without assistance. The intensity escalated when we moved him closer to us. That was the turning point—when caregiving shifted from something we managed at a distance to an integral part of our daily lives. Q: What was the moment caregiving shifted from a private responsibility to something that affected your work identity? Hofer: In the final years, I relied heavily on my team and my manager. I shared just enough to get support, but kept most of it private. The reality was too painful. And there was always a risk that sharing more broadly could change how I was perceived—a sad but very real part of corporate culture. When my father passed, everything surfaced. The physical effects of burnout and grief became impossible to ignore. I had powered through for years, but the cost showed up in my body and my ability to stay engaged. That’s when I realized: The effects of caregiving last long after the role ends. Q: Did caregiving change how you lead? Hofer: Absolutely. It deepened my understanding of trust, delegation, and succession planning—I had to rely on others in ways I hadn’t before. And it made me realize that our existing leadership structures need to be rebuilt because they did not factor in the reality of caregiving. More importantly, it changed how I view performance. High-performing employees are often carrying invisible weight. Leadership isn’t just about driving results—it’s about understanding context and creating systems that allow people to perform sustainably and consistently. Q: How did you handle burnout, and what workplace lessons did you take from it? Hofer: Burnout is not something you can outwork. Staying busy doesn’t fix it. When it surfaces, you have to listen and act. It’s a signal, not a weakness. From a workplace perspective, leaders must normalize conversations around capacity. Ignoring burnout doesn’t protect performance—it erodes it over time. Q: What do employers still misunderstand about employees who are caregivers? Hofer: They’re only seeing part of the story. If someone’s performance or engagement shifts, there’s often more beneath the surface. Caregiving is unpredictable and rarely linear. Leaders need to ask questions—with empathy—and create space for honest answers. Q: How can companies move beyond talking about work-life balance to actually supporting eldercare challenges? Hofer: It comes down to lived values. If organizations say they prioritize people, leaders have to model that. Flexibility cannot exist only in policy—it has to show up in daily behavior. Employees notice the gap between what’s said and what’s rewarded. Q: What should leaders do now? Hofer: Eldercare is not a future issue—it is a present reality. For organizations to effectively support a workforce increasingly shaped by caregiving responsibilities, leaders must move beyond awareness and into action. Three shifts are critical: 1. Normalize transparency around capacity 2. Redefine performance through context, not just output 3. Align culture with practice, not just policy. Organizations that continue to treat caregiving as an unspoken issue will face rising burnout, disengagement, and talent loss. Those that acknowledge it as a shared reality—and design cultures, expectations, and systems accordingly—will build stronger, more resilient, and more human organizations. Caregiving does not just change individuals. It is reshaping the workforce. The leaders who understand and act on that will define the next era of leadership. Larraine Segil is founder, chair, and CEO of The Exceptional Women Alliance. View the full article

Kanishka Narayan says Britain needs to ‘make most of opportunities’ as government launches £500mn unit View the full article

Over the past few years, words that once had no place in workplace conversations have slowly entered HR agendas: menstruation, endometriosis, perimenopause, menopause, breast cancer and—more slowly—male andropause or prostate cancer. These are not passing trends. They signal a deeper shift in how we understand work and the people who do it. For decades, work was designed around a fiction, that of the “neutral” worker, an abstract individual assumed to be fully available, consistent, rational, and unaffected by bodily constraints. But this neutrality was never real. As Caroline Criado Perez has shown in her brilliant book Invisible Women, many systems and environments have been designed around a male body treated as the default. And that includes workplaces. Hence, the implicit expectation is that women adapt to a model never designed for them, to organizational structures, as well as tools and equipment. Yet people do not leave their bodies at the door when they enter the workplace. Hormonal cycles, pregnancy, postpartum recovery, menopause, and andropause are not “private” issues without professional consequences. They affect energy levels, cognitive load, availability, and sometimes long-term career trajectories. Their historical invisibility has come at a huge cost (albeit largely ignored) both for individuals and for organizations. It is essential to note that gendered health is not only about biological differences. It also reveals how work itself is structured. Women, for example, experience higher rates of musculoskeletal disorders, partly because they are overrepresented in repetitive jobs and are more likely to carry a disproportionate share of unpaid caregiving and domestic labor. Work is never experienced in isolation. It is embedded in real lives, with cumulative fatigue, constraints, and vulnerabilities. Gendered health, in that sense, sits at the intersection of biology and the sociology of work. Persistent taboos Taboos are slowly shifting. Menstruation, endometriosis, and menopause are more visible in public debate than they were a decade ago. Yet in many organizations, silence remains the norm. Many women remain cautious about speaking openly, all too aware of the risk of being reduced to sexist stereotypes. Male themes are as invisible if not more. Andropause, i.e. the gradual testosterone decline associated with aging, is less socially recognized than menopause. Its invisibility reinforces the myth that only women’s bodies are a “problem”. In reality, aging affects everyone. As workforces age and careers extend, organizations are increasingly confronted with more diverse, uneven, and non-linear health trajectories. There is a structural tension here. Acknowledging gendered health can trigger unintended consequences. It can reinforce bias by framing women as less stable. In some cases, even well-designed policies—such as leave for endometriosis or pregnancy loss—are underused because employees fear stigma. In cultures that remain implicitly sexist, formal rights do not automatically translate into real usage. A more universal approach is required This is why a purely category-based approach has its limitations. A more effective lens may be a more universal approach to vulnerability. Rather than segmenting workers into fixed groups (“women,” “seniors,” “caregivers”), it may be more accurate to focus on life “moments”. Work lives are punctuated by predictable and unpredictable disruptions: disease, grief, separation, caregiving responsibilities, burnout, recovery periods. These situations are widespread and recurrent. Today, a majority of workers are caregivers in one way or another. And there will be more and more of them with population aging. At the same time, careers lengthen and transitions multiply. Therefore the stable, continuous and homogeneous industrial model of work no longer reflects reality. This is where the idea of universal design comes in handy. Originally developed in disability studies, it proposes designing systems starting from the most constrained users. In practice, what improves accessibility for people with disabilities often improves usability for everyone. Applied to work, this logic invites us to rethink schedules, career paths, flexibility, and recovery periods—not as exceptions, but as structural features. The goal is robustness through inclusivity. And we need to think differently about age All this also requires revisiting how we think about age. Behind gendered health lies another blind spot: ageism. We still tend to treat chronological age as a proxy for capability. Yet age is a weak indicator of health, energy, or engagement. With longer life expectancy, variation within age groups is widening. A 60-year-old worker may be fully capable—or significantly constrained. The average says increasingly little. Long term the model of the “ideal worker” is unsustainable. This fully available, always-healthy, unencumbered employee has never been representative of reality. But demographic change is making this fiction even less viable. Organizations should now be managing diversity of conditions as a structural norm. That’s why gendered health is not a niche topic. It is an entry point into a broader question: how durable is work as currently designed? Recognizing bodies, ages, and life moments is the very condition for organizational resilience. View the full article

As the capabilities of frontier models advance, gaining access to the technology could become critically importantView the full article

Have you noticed that in the current discourse around artificial intelligence, the narrative often slips into one of two extremes? There is either a techno-utopian dream of total automation or a dystopian nightmare where human agency is erased. But there are other options! As we navigate this inflection point in civilization, I invite you to consider a third path: pragmatic optimism. And that’s because we are currently in the midst of a human revolution, not a tech revolution. The most successful organizations of 2026 and beyond will not be those that simply use AI to do more things faster. Instead, they will be the ones that use AI as a creativity accelerator, freeing up human capacity for the work that only we can do: imagining, connecting, and creating meaning. Optimistic macroeconomic forecasts, such as those from Citrini Research, suggest that AI could trigger a global intelligence boom, driving sustained productivity growth and real wage gains, provided that we treat machine intelligence as complementary to human judgment and truth. To ensure this virtuous cycle benefits everyone, leaders must move beyond managing compliance and begin cultivating the conditions and structures where creativity and psychological safety can flourish. By integrating the principles of Move, Think, and Rest (or MTR, pronounced “motor”) into the core of organizational culture, we can build creative resilience and work with AI rather than be displaced by it. Here are three calls to action to help us remain the architects of our own future. 1. Design in Friction In a world obsessed with seamless automation, friction is often viewed as a bug. But friction is where learning happens. To Move within this principle, become a “clumsy student” of something physical. Embodied learning, the kind that comes from using your hands or your body, is irreplaceable and builds what I call sentient intelligence. To Think, resist the urge to automate everything immediately. Instead, struggle with ambiguity: read fiction, ask “But… why?” Follow the question past the obvious answer. And to Rest, pause before automating any workflow. Rather than simply making an old process faster, use that pause to reimagine the workflow entirely- much like the shift from steam to electrification required a total redesign of factory floors, not just faster steam engines. The leaders who will thrive are not those who eliminate friction. They are those who learn to distinguish productive friction, the kind that generates insight, from the kind that merely generates frustration. Remember, at the end of the day, friction yields energy! Nissan’s former head of design, Jerry Hirshberg, called this “creative abrasion”. 2. Protect Serendipity As digital experiences accelerate, high-touch, analog encounters will become the new premium. To Move toward serendipity, prioritize in-person interactions that allow for the “creative abrasion” necessary for innovation. Ideas that change industries rarely emerge from a Slack thread. To Think, lean into fun ambiguity. Seek out conversations and encounters that don’t have a predetermined outcome. That’s where the most original thinking is born. To Rest, I recommend the Dutch practice of Niksen: the deliberate art of doing nothing, together. It is in the quiet space between people that human connection deepens and unexpected insights surface. Serendipity is not accidental. It is a design choice. Organizations that schedule unstructured time, invest in physical space for collision, and resist the urge to fill every moment with an agenda are making a bet on human creativity that no algorithm can replicate. 3. Pay Attention The speed of AI should buy us time, not just fill it with more tasks. To Move, lean into what is real: touch, taste, nature, smell. These are not indulgences; they are vital inputs of sentient intelligence that no large language model can access. To Think, be rigorous in daily self-assessment. Ask yourself: “Is this tool sharpening my thinking, or is it replacing it?” The answer should make you uncomfortable at least occasionally. That discomfort is useful data. To Rest, be intentional about the time you reclaim. If AI returns hours to your day, the question is not what to fill them with, but what deserves that space. Attention is the scarcest resource in the Imagination Era. The leaders and organizations that protect it structurally, culturally, and personally- will have a durable advantage over those who simply automate their way to busyness. The Architects of Our Own Future Ultimately, AI should be our co-pilot, amplifying what makes us uniquely human. By being intentional about how we design friction, protect serendipity, and pay attention, we can ensure that the “intelligence boom” leads to true human flourishing rather than the mere acceleration of the status quo. The Imagination Era is not something that will happen to us. We must call upon our own agency to ensure that we are building a human-centric future, one decision at a time. The question is whether those decisions will be made by default or by design. View the full article

ChatGPT citations favor pages that rank well, match the query in their headings, and stay tightly focused, according to an AirOps study of 16,851 queries. The top retrieval result was cited 58% of the time, and pages that answered the main query more narrowly outperformed broader, more comprehensive guides. Why we care. This study clarifies how to earn ChatGPT citations: win retrieval, mirror the query in your headings, and answer one question extremely well. In this study, that mattered more than breadth. The findings. Retrieval rank was the strongest signal. Pages in the top search position were cited 58.4% of the time, versus 14.2% for pages in position 10. Heading relevance was the strongest on-page factor. Pages with the strongest heading-query match were cited 41.0% of the time, compared with roughly 30% for weaker matches. Focused pages also beat comprehensive ones. Pages that answered the main query more narrowly outperformed broader, more comprehensive guides, undercutting the usual “ultimate guide” approach. What drove ChatGPT citations. In this study, pages that won citations usually ranked well, used headings that closely matched the query, and stayed focused on answering it. Structure helped, but only slightly: Pages with JSON-LD markup posted a 38.5% citation rate versus 32.0% for pages without it, and articles with 4 to 10 subheadings performed best. Beyond a certain point, length hurt performance: Pages between 500 and 2,000 words performed best, but pages longer than 5,000 words were cited less often than pages under 500 words. Freshness helps, up to a point. Pages published 30 to 89 days earlier performed best, while pages newer than 30 days performed worse. This suggests new content may need time to build retrieval signals. Pages more than 2 years old were cited less often, which suggests that content refreshes could help if you’re already ranking for the right queries. About the data. AirOps said it scraped ChatGPT’s interface, not the API, and analyzed 50,553 responses generated from 16,851 unique queries run three times each. The dataset included 353,799 pages and more than 1.5 million fan-out detail rows across 10 verticals and four query types. The study. The Fan-Out Effect: What Happens Between a Query and a Citation View the full article

We may earn a commission from links on this page. As HBO's Industry begins, the recent grads working at prestigious investment bank Pierpoint & Co. are given their marching orders: There are a lot of them and only a few full-time job openings, so they'll need to prove themselves if they hope to stick around. They respond to this challenge by doing whatever it takes—whatever it takes. Renewed for a fifth and final season, Industry has been the streaming era's most cogent take on the world of finance bros (of any gender), and modern white collar workers in a more upscale Glengarry Glen Ross mode. While you wait for this story of disaster capitalism to return, dive into these 10 other shows that make work look even more stressful than it is. Sweetbitter (2018 – 2019) Taking on restaurant culture in place of finance, Sweetbitter finds much of the same stress, intensity, and competitiveness non display—probably no surprise if you watch The Bear. The show is adapted from the Stephanie Danler novel of the same name; she based it on her experiences as an NYC waitress (she also created the series and wrote the pilot), so we can assume a certain level of verisimilitude. Yellowjackets' Ella Purnell plays Tess, 21 at the series' opening and arriving in the city with big dreams. She gets a job at a prestigious restaurant where, as we (and she) quickly learn, there's at least as much drama (including drugs, booze, and sex) on the service side of the industry as there is in the kitchen. Stream Sweetbitter on Prime Video. Sweetbitter (2018 – 2019) at Prime Video Learn More Learn More at Prime Video Misaeng: Incomplete Life (2014) A critically-acclaimed sensation on its initial release, there's a really impressive, stressful, universal sense of realism in this show about white-collar work in South Korea. Im Si-wan is Jang Geu-rae, a young man who has done nothing but work toward his goal of becoming a professional Baduk (a game which you might know better as Go) player since childhood. By his 20s, though, it's clear that it's not to be, and all he can do is take an office job as a provisional contractor with a shipping company. A complete outsider, he's even less prepared than the other interns for a high-stress world in which a work-life balance is all but impossible. Its intensity is very much in the mode of Industry, but there's hopefulness in Geu-rae's determination not to lose himself. Stream Misaeng: Incomplete Life on Netflix and Tubi. Misaeng: Incomplete Life (2014) at Netflix Learn More Learn More at Netflix Billions (2016 – 2023) Less young-Brit oriented and more of a cat-and-mouse game, this one is a (darkly) satirical dive into the shady world of high finance. Paul Giamatti is rather ruthless as U.S. attorney Chuck Rhoades (based in part on the real-life Preet Bharara), who is working to bring down shady hedge fund manager Bobby Axelrod (Damian Lewis). The tone is that of a darkly comic soap opera, and it stays fresh over seven seasons by playing off the contrast between Axelrod's willingness to use all the money and power at his disposal to stay on top and out of jail, and Rhoades' willingness to resort to shady, not-entirely-legal tactics to reel in his big fish. Stream Billions on Paramount+ and Prime Video. Billions (2016 – 2023) at Paramount+ Learn More Learn More at Paramount+ How to Make It in America (2010 – 2011) A bit of counter-programming, perhaps, in this dramedy about a couple of scrappy New York City outsiders who would never fit in with the Industry crew. And yet! There's a sense here that getting ahead requires tremendous hustle, and that drive for big success carries with it the potential for an even bigger fall. Bryan Greenberg is Ben Epstein, a quiet guy with any number of big ideas, while Victor Rasuk is outgoing, often shameless, Cam Calderon—together they manage a startup clothing business with the benefits of neither money nor experience, amiably hustling their ways to success, maybe. It's like Industry if that show were about nicer, goofier guys who are at their best when talking themselves out of trouble. Stream How to Make It in America on HBO Max and Netflix. How to Make It in America (2010 – 2011) at HBO Max Learn More Learn More at HBO Max The Dropout (2022) The passage of time has made the story of Theranos founder and fraudster Elizabeth Holmes feel positively quaint, not least because a few of her high-profile backers are serving in the current White House. Amanda Seyfried plays the "entrepreneur" whose rise and precipitous fall has already been the subject of a handful of documentaries. It starts at age 18, when she dropped out of Stanford to build a startup around an at-home blood testing machine that didn't work even a little bit. Years of charming big-name investors with big promises, lies agreed upon, and cleverly faked results lead to big money for Theranos and a lot of bad diagnoses for patients. Stream The Dropout on Hulu. The Dropout (March 3) at Hulu Learn More Learn More at Hulu WeCrashed (2022) Another true-life story of big business and a big fall, this one stars Jared Leto, Anne Hathaway, and Kyle Marvin as the co-founders of WeWork, the (eventually) billion-dollar company that leases co-working spaces. The focus is on Leto and Hathaway's Adam and Rebekah Neumann, portrayed as simultaneously delusional and calculating, operating as almost toxically nice cult leaders while firing people for stepping into their eyelines at the wrong time. The title's crash comes when the company files an now-infamous S-1 form to go public, confidently documenting big losses, extremely precarious financial arrangements, and the weird relationship between the Neumanns and the larger company. Don't worry, though, this one has a happy ending: WeWork lost billions but the Neumanns remain very, very rich. Stream WeCrashed on Apple TV. WeCrashed (2022) at Apple TV Learn More Learn More at Apple TV Mad Men (2007 – 2015) One of the deservedly big names in prestige TV, Mad Men feels, in many ways, like the blueprint for Industry; each creates characters with novelistic depth in wildly cynical and intense environments. The mid-century modern stylings of a New York City ad agency make for a contrast with that of a modern London finance house, but the pressure-cooker environments and excesses feel very much in line. Stream Mad Men on HBO Max. Mad Men (2007 – 2015) at HBO Max Learn More Learn More at HBO Max Boiling Point (2023) Back to the restaurant industry: BBC One's Boiling Point serves as a direct sequel to the 2021 film, though it doesn't really require you to have seen the original—all you really need to know is that the movie's main character, Andy Jones (Stephen Graham) is recovering from a stress and substance abuse-fueled heart attack, while series lead and Andy's former sous chef Carly (Vinette Robinson) has poached much of the old staff to start a new operation called Point North. The show navigates between the high-pressure environment of a restaurant start-up and the personal lives of its staff, while Andy's fall from grace smartly offers a glimpse of a possible future for the driven staff. Stream Boiling Point on Prime Video. Boiling Point (2023) at Prime Video Learn More Learn More at Prime Video Skins (2007 – 2013) I'm calling this one a prequel to Industry (it absolutely is not), in that it deals with similar themes in a (largely) high school environment. Skins makes clear that adolescence is an absolute pressure-cooker, and it feels as though any number of these intense, competitive, often hard-partying characters could graduate into Industry—and, in fact, the final season of Skins sees one-time Kaya Scodelario's Effy Stonem take a job in finance (and dabble in insider trading) as part of an arc that feels very much like Industry in miniature. The popular and controversial British series launched names like Nicholas Hoult, Daniel Kaluuya, and Dev Patel while dealing with hot-button issues like mental illness, substance abuse, and bullying. Look for Freya Mavor (Industry's Daria Greenock) in both shows. Stream Skins on Hulu. Skins (2007 – 2013) at Hulu Learn More Learn More at Hulu Halt and Catch Fire (2014 – 2017) A show that largely flew under the radar during its four seasons, this one offers up a (heavily) fictionalized portrait of the rise of personal computers in the 1980s, into the early days of broad adoption of the internet in the '90s. Lee Pace plays Joe MacMillan, the antihero lead who leaves IBM in 1983 to join the fictional Cardiff Electric. He's charismatic, manipulative, and not terribly tech-proficient, but nonetheless has dreams of building the next big tech innovation—starting by reverse-engineering the IBM PC. It's a show that comes up on any number of critical best-of lists and has a sick opening sequence. And did I mention Lee Pace? Stream Halt and Catch Fire on Prime Video. Halt and Catch Fire (2014 – 2017) at Prime Video Learn More Learn More at Prime Video View the full article

Google announced updates to Chrome that let searchers use AI Mode in a more “engaging” and “deeper” way. Chrome lets you do all of this without switching tabs and potentially losing your place. What is new. Chrome added these new features: (1) Search side-by-side: When you’re using AI Mode in Chrome desktop, clicking a link will open the webpage side-by-side with AI Mode. This makes it easier to visit relevant websites, compare details, and ask follow-up questions while maintaining the context of your search. Here is what it looks like: (2) Search across your tabs: On Chrome desktop or mobile, you can tap the new “plus” menu on the New Tab page (or the existing plus menu in AI Mode) to select recent tabs and add them to your search, allowing AI Mode to provide tailored responses and suggest more sites to explore. (3) Multi-input and easy tool access: You can also mix and match multiple tabs, images, or files like PDFs and bring that context into AI Mode. Additionally, tools like Canvas or image creation, are accessible wherever you see the new plus menu in Chrome. Why we care. These are some new Chrome specific features for users in the U.S. English language that help you unlock more AI Mode features. Again, it is specific to Chrome users right now but it does show you the direction Google is taking AI Mode. View the full article

New Ahrefs data shows Reddit pages appeared often in ChatGPT retrievals but rarely as visible citations. The post ChatGPT Often Retrieves But Rarely Cites Reddit Pages, Data Shows appeared first on Search Engine Journal. View the full article

This week's Freddie Mac mortgage rate survey shows rates at the lowest in four weeks, but homebuyers are giving mixed signals even with improved purchase power. View the full article

Microlending platforms serve as a bridge between borrowers, often from underserved communities, and lenders ready to provide small loans, typically under $50,000. These platforms operate through various models, such as peer-to-peer lending and institutional microfinance. Borrowers submit applications detailing their financial needs, and upon approval, they receive funds to support their ventures. Comprehending how these platforms function can reveal their potential benefits and challenges for aspiring entrepreneurs. What factors should you consider before applying for a microloan? Key Takeaways Microlending platforms connect borrowers, often from underserved communities, with lenders willing to provide small loans, typically under $50,000. They utilize various models, including peer-to-peer financing and institutional lending through microfinance organizations. Platforms like Kiva and Accion Opportunity Fund offer loans alongside financial education and business training to enhance borrower success. Approval processes typically involve evaluating personal and business financial details, allowing access even for those with low or no credit scores. Microlending platforms aim to empower individuals and promote financial independence while managing risks such as high interest rates and potential default. What Is Microlending? Microlending is a financial practice that provides small loans, typically under $50,000, to individuals and entrepreneurs who mightn’t qualify for traditional banking services. Originating with Grameen Bank in 1976, microlending aims to empower marginalized groups, particularly women, by offering them vital financial resources for business ventures. Unlike traditional loans, microloans have less stringent qualification criteria, making them accessible to people with limited or no credit history. Interest rates usually range from 6.5% to 15%, with repayment terms from one to five years, depending on the lender’s policies and the borrower’s creditworthiness. Today, microlending platforms, often utilizing p2p platforms, connect borrowers directly with lenders. These digital platforms streamline the loan application process as they evaluate creditworthiness through alternative data points, enhancing the chances for underserved communities to secure needed funding. Consequently, microlending plays a significant role in promoting financial inclusion and economic empowerment. How Microlending Works When you consider microlending, it’s important to understand the application process and how funding works. You’ll typically need to fill out an application and provide documentation, which can lead to funding in about 30 to 90 days. Once approved, you’ll repay the loan in installments, with interest rates varying based on your creditworthiness and the lender’s policies. Application Process Overview Applying for a microloan involves several key steps that you should comprehend before proceeding. First, you’ll need to choose a microlender and complete an application, which typically requires personal and business details along with supporting documents. Once submitted, the approval process can take anywhere from 30 to 90 days, during which lenders assess your creditworthiness and business viability. Microloans usually range from $500 to $50,000, with the average amount around $13,000; interest rates often vary between 6.5% and 15%. Furthermore, many microlending platforms connect borrowers with lenders, including options for peer-to-peer lending, where multiple investors can fund a single loan. Comprehending these steps will help you navigate the microloan application process effectively. Funding and Repayment Structure For those seeking small loans, microlending offers a unique funding structure that caters to individuals who may struggle to access traditional credit. Typically, microloans range from $500 to $50,000 and have simpler qualification criteria. You can apply through various microlending platforms, which evaluate your creditworthiness using personal and business data. Once approved, repayment terms usually vary from 1 to 7 years, depending on the lender and loan specifics. Interest rates can range from 6.5% to 15%, though some platforms, like Kiva, offer 0% interest loans funded by crowdfunding. Generally, the application process takes 30 to 90 days, and you’ll need to repay the loan in installments according to the agreed terms. Borrower Qualifications for Microloans Comprehending borrower qualifications for microloans is essential if you’re considering this financing option to support your business endeavors. Microloans have specific criteria that potential borrowers must meet, which can vary by lender. Here are some common qualifications: Basic credit assessment, with low or no credit scores often accepted Evaluation of personal income, business revenue, and operational duration Documentation to verify income sources, business plans, and intended use of funds Focus on supporting underserved groups, like women and minorities Targeting new entrepreneurs or small business owners Understanding these qualifications can help you prepare your application effectively. Since microlenders prioritize business plans and revenue potential, focusing on these aspects can improve your chances of securing the funds you need. Types of Microlending Models During the exploration of the domain of microlending, it’s important to understand the various models available, as each offers distinct features and benefits. The primary models include peer-to-peer (P2P) financing and institutional lending through microfinance organizations or nonprofits. In the P2P model, you can connect directly with individual borrowers, often selecting them based on their profiles and loan requests. This approach allows for pooling funds from multiple lenders to meet the loan amount. On the other hand, institutional lenders, like microfinance institutions (MFIs), typically focus on social impact, offering loans along with business training and support. Some platforms, such as Kiva, implement crowdfunding to gather capital for microloans, enabling individual investors to fund small portions of loans with zero interest and flexible repayment terms. Each model has its own eligibility criteria and loan structures, with microloans usually ranging from $500 to $50,000, aimed at those who may struggle with traditional financing. Benefits of Microlending for Borrowers Microlending offers you easier access to capital, especially if you struggle to qualify for traditional loans. With flexible repayment terms and amounts customized to your needs, you can manage your finances more effectively. Furthermore, many microlending platforms provide financial education opportunities, helping you build the skills necessary for long-term success. Easier Access to Capital Access to capital can be a significant hurdle for many aspiring entrepreneurs, especially those from underserved communities. Microlending platforms ease this challenge by providing funding options typically ranging from $500 to $50,000. These platforms often prioritize social impact, allowing individuals with limited credit histories to secure loans. Here are some key benefits of microlending: Flexible qualification criteria cater to diverse backgrounds. Lower interest rates, usually between 6.5% and 15%, make financing more affordable. Shorter repayment periods of 1 to 5 years help manage cash flow. Access to additional resources, such as business training and mentorship. Empowerment of small business owners who mightn’t qualify for traditional loans. These factors make microlending an appealing opportunity for many. Flexible Repayment Terms For many borrowers, the flexibility of repayment terms offered by microlending platforms can greatly ease the financial burden often associated with loans. These platforms typically allow you to choose repayment timelines that fit your unique situation, often ranging from 1 to 5 years. This adaptability helps reduce financial pressure when compared to traditional loans with rigid structures. Furthermore, many microlending platforms report your repayments to credit bureaus, which aids in building your credit history. With interest rates usually between 6.5% and 15%, microlending offers competitive options. The average loan amount is around $13,000, making it manageable for small businesses to repay without overextending financially. This flexibility finally empowers you to make informed financial decisions. Financial Education Opportunities How can financial education transform your borrowing experience? Microlending platforms often provide valuable resources that improve your financial management skills. These initiatives empower you, especially if you’re a woman entrepreneur, by offering not just loans but also extensive training. Here are some key benefits you’ll gain from financial education through microlending: Access to business training workshops Mentorship opportunities in budgeting and financial planning Improved credit scores from timely repayments Upgraded operational management skills Connections to community networks for ongoing support Risks and Challenges in Microlending Though microlending platforms provide essential financial services to underserved populations, they come with notable risks and challenges that borrowers and investors should carefully consider. Microlending often involves higher interest rates, ranging from 7.99% to 35.99%, reflecting the increased risk of lending to individuals without traditional credit histories. Borrowers frequently face short repayment terms of 1 to 5 years, which can create financial strain if their cash flow is limited. Significant default rates may result in little or no recovery for lenders, as economic factors can hinder borrowers’ ability to repay. The lack of collateral requirements can encourage over-borrowing, leading to unsustainable debt levels. Furthermore, high service fees associated with these platforms can diminish overall returns for investors, making microlending a riskier investment option compared to traditional lending. Careful assessment of these factors is vital for anyone considering participation in microlending. Leading Microlending Platforms As you explore the scenery of microlending platforms, you’ll find several leading options that cater to diverse borrower needs and investor preferences. These platforms provide unique advantages and target different demographics, making them valuable resources in the microlending environment. Here’s a look at some of the top players: Kiva: Offers interest-free loans starting at $25, supporting entrepreneurs globally. Accion Opportunity Fund: Provides microloans from $5,000 to $100,000, focusing on diverse business owners and offering financial education. Grameen America: Targets women entrepreneurs with loans starting at $2,000 and emphasizes community support. LendingClub: Facilitates peer-to-peer lending for loans ranging from $1,000 to $40,000, with flexible terms. Upstart: Requires accredited investors, offering loans with a minimum investment of $100 and terms of three or five years. These platforms illustrate the variety and adaptability found within the microlending sector. How to Apply for a Microloan Have you ever wondered what it takes to secure a microloan? The application process begins with checking your eligibility, which involves evaluating your personal credit score, annual revenue, and existing debt. You’ll typically complete an online form that requires both personal and business information, alongside uploading supporting documents. Here’s a quick overview of the steps: Step Description Timeframe Check Eligibility Evaluate credit score, revenue, and debt status Before applying Complete Application Fill out online form and upload documents 1-2 hours Approval Process Wait for processing and verification A few days to weeks Repayment Terms Understand repayment schedule and terms 6 months to several years Once approved, you’ll need to repay the loan in installments according to the lender’s terms. Is Microlending Right for Your Business? Is microlending the right choice for your business? If you’re a small business owner needing quick access to funds, microlending may be a viable option. These platforms offer loans between $500 and $50,000, often with flexible qualification criteria. Nevertheless, consider the following: Average microloans are around $13,000, suitable for inventory, payroll, or operational costs. Approval times can take 30 to 90 days, impacting immediate cash flow. Borrowers may face higher interest rates, typically between 6.5% and 15%. Repayment terms are usually shorter, ranging from 1 to 5 years. Microlending can support underserved communities, including women and minorities. Evaluate your business needs and financial situation carefully. Microlending can provide valuable resources, but it’s crucial to understand the implications of borrowing before making a decision. Frequently Asked Questions What Is Microlending and How Does It Work? Microlending is a financial practice where small loans, usually under $50,000, are provided to individuals or businesses lacking access to traditional banking services. You apply through a microlender, and the funding process typically takes 30 to 90 days. Interest rates range from 6.5% to 15%, with repayment terms from one to five years. Lenders evaluate your creditworthiness using various factors, often relying on alternative data because of limited credit histories. Who Typically Uses Micro Lending? You’ll find that microlending is often utilized by entrepreneurs, particularly those from underserved communities. Small business owners, including many women, seek these loans to start or grow their ventures when traditional JPMorgan Chase deny them access. Typically, individuals with low incomes or poor credit scores turn to microlending platforms, as they offer flexible eligibility criteria. Loan amounts can range from $500 to $50,000, with the average microloan being around $13,000. What Is the Best Example of Micro Financing? The best example of microfinancing is Kiva, which offers interest-free microloans starting at $25 to borrowers worldwide. You can lend directly to entrepreneurs in underserved communities, encouraging financial inclusion. Kiva’s crowdfunding model shows a repayment rate over 96%, emphasizing its effectiveness. What Is Micro Financing and How Does It Work? Microfinancing is a financial service providing small loans, usually under $50,000, to individuals or businesses that lack access to traditional banking. You apply through a microlender, submit necessary documentation, and receive funding within 30 to 90 days. Repayment occurs in installments with varying interest rates, typically between 6.5% and 15%. These loans can be used for business needs like inventory or operational costs, but not for settling debt or purchasing real estate. Conclusion In conclusion, microlending platforms provide vital financial support to underserved borrowers, enabling them to access small loans for their entrepreneurial needs. By comprehending how these platforms operate, the types of loans available, and the qualifications required, you can make informed decisions about your financing options. During microlending offers numerous benefits, it’s important to evaluate the associated risks and challenges. In the end, appraising whether microlending aligns with your business goals can help you leverage this resource effectively. Image via Google Gemini and ArtSmart This article, "What Are Microlending Platforms and How Do They Function?" was first published on Small Business Trends View the full article

A reader writes: One of my employees has asked for a massive raise. He has good reasons for wanting a raise: his responsibilities have ended up being very different than what he was originally hired for, he’s been doing very well with them, and he’s definitely paid below market for what he’s ended up doing. We hired him at $15/hour for an entry-level position with no hard requirements, and based on some quick market research, I’d say the work he’s doing now is closer to a $20-$25 range, so I’m actually in favor of giving him a pretty substantial increase. The trouble is that he’s asked for an increase to $40/hour, and he’s only been here for four months. That’s more than I make, and I’m honestly shocked that he thought this was reasonable to ask for. He says he did some market research, but that number hasn’t been supported by anything I’ve been able to find. Four months also seems like a short amount of time to me, but I don’t know if the significant change in duties should override that. I want to advocate for my employee with our company’s owner (who is very reluctant to spend money), but I am suspicious that bringing the employee’s $40/hour request to him will make my employee (and potentially me as well) look completely out of touch with reality. Our owner is extremely hands-off — we’re all remote, and I talk to him maybe once every month or two for about 10 minutes. I told my employee that $40/hour was more than I make and gently suggested that asking for a lower number might be a better idea, but he shrugged that off and said he isn’t set on that number, but sees it as a good “starting point.” Any suggestions for how to approach this? I answer this question over at Inc. today, where I’m revisiting letters that have been buried in the archives here from years ago (and sometimes updating/expanding my answers to them). You can read it here. The post my employee asked for a 170% raise appeared first on Ask a Manager. View the full article

Modern-day Luddites are gaining ground because tech titans haven’t shown people how innovation will improve their livesView the full article

And why it changes everything for you. By Hitendra Patil Client Accounting Services: The Definitive Success Guide Go PRO for members-only access to more Hitendra Patil. View the full article

And why it changes everything for you. By Hitendra Patil Client Accounting Services: The Definitive Success Guide Go PRO for members-only access to more Hitendra Patil. View the full article

We may earn a commission from links on this page. “Be able to do a pull-up” is a common fitness goal, and if you work hard—with negative pull-ups, inverted rows, and more—someday you’ll get there. Go ahead, take a minute to celebrate. But don’t drop the workouts that you were doing pre-pull-up. It’s tempting to change up your training, because for weeks or months (maybe years!) you were doing the things that you do when you can’t do a pull-up. You may have been doing negative pull-ups, where you start at the top of the movement and slowly lower yourself down. You may have been doing inverted rows, where you pull yourself toward a low bar or rings. You may have been doing assisted pull-ups on a machine, banded pull-ups with decreasing thicknesses of elastic, lat pulldowns, dumbbell rows, and more. But your first pull-up is not a graduation from all of that. You should not leave the resistance bands and the lat pulldown machines in the dust. They need to stay with you during the next phase of your journey. JFIT Deluxe Doorway Pull-Up Bar $30.76 at Amazon $43.99 Save $13.23 Get Deal Get Deal $30.76 at Amazon $43.99 Save $13.23 Why you might not be able to consistently do a pull-upSo you did a pull-up today. That doesn’t mean you’ll be able to do one tomorrow. That’s probably confusing, so let me explain. We all have a range of abilities that we can do on any given day. For example, if you squatted 225 pounds last week, that doesn’t mean you could also squat 225 today. We might say that your “range” is 200-225, and when you’re well-rested and psyched up, you’re able to hit the top of that range. But even on a bad day, you know you can hit at least 200. Pull-ups are like that, too. Maybe when you started working toward a pull-up, your strength was in the range of 50-55% of what it takes to do a pull-up. That means that when you get your first pull-up, your range might be something like 95-100%. The day you did the pull-up is a 100% day. The next day, maybe you’re only at 99%. You’ll wonder why you “can’t” do one anymore. What you need to do now is keep working until doing one pull-up is the bottom of your range of abilities. If you’re hovering between being able to do 0-1 pull-ups, you want to expand that range until it’s about 1-3 pull-ups. By the time you can do two or three pull-ups some days, you’ll be able to do one pull-up any day. By the way, everything I’m saying applies to chin-ups, as well. (A pull-up has your palms facing away from you; a chin-up is with palms toward you.) Chin-ups are slightly easier than pull-ups, so if you can do a pull-up sometimes, you might already be able to do chin-ups pretty consistently. Feel free to mix chin-ups and pull-ups in your training. How to get your second pull-upGetting that first pull-up doesn’t unlock a whole new world of workouts; it just gives you one extra tool. You already have a variety of exercises you currently do that build your pull-up strength, and you can do those exercises at a variety of rep ranges and difficulty levels. To that, you can add “do one pull-up.” That one pull-up is not enough to replace everything else. If you need a refresher on great pull-up accessories, they include: Negative pull-ups (slowly lowering yourself down). You can do these for reps, or you can aim to make each set a single, ultra-slow, perhaps 10 or 15 second motion. Banded pull-ups (with a resistance band supporting your feet—either hanging from the pull-up bar or stretched across the rack underneath you). You can do more reps with a heavier band, or fewer reps with a lighter band. These work best when done as a slow, controlled rep. Box or bench pull-ups, with one or both feet on a surface underneath you. Push with your foot just as much as you need to complete each rep. The lat pulldown machine or the assisted pull-up machine. Both of these work your upper body pulling muscles, although they aren’t as effective at training your core or your body position. Rows, rows, rows. My favorites are Kroc rows with dumbbells that are so heavy you need to “cheat” by twisting your whole body (this is a good thing, since it gets your core working). Other great rows include barbell rows, seated cable rows, bent-over dumbbell and kettlebell rows, and bodyweight inverted rows. When you’ve finished your other pull-up accessories for the day, do a few sets of rows. Your pull-up program may have included other exercises as well, like planks and other core work, grip training, dead hangs, or maybe even stretches for your shoulders. Keep doing those, too. If you’ve only been doing one or two of the things from the list above, feel free to add one or two more. Do not feel like you have to do all of them. I’d pick one of the pull-up variations each day—negatives, banded, or bench-assisted—and then add two more exercises from the rest of the list (one machine and one row, or two different rows). How to do more and more repsThat singular pull-up you can do, at least sometimes? Definitely do it at the beginning of your workout. One pull-up, rest a minute or two, then attempt it again. Once you fail, move on to the rest of your workout—the negatives and rows and so on. If you can do a pull-up more than once in a day, you’re getting close to being able to do two or three in a set. If you do a pull-up and it doesn’t feel like a struggle, go for a second rep. Soon enough, you’ll be hitting sets of two or three. Once you can consistently do at least three pull-ups, you can start making this more of a cornerstone of your workouts, rather than a fun bonus. Do three sets of three every day that you do upper-body exercises, and it’s now that you can drop one of your other pull-up exercises. (Still, keep the rows in.) At this point, if you want something more intensive that has you doing pull-ups almost every day, consider the “3RM” version of the Fighter Pull-up Program. Once you can do sets of five consistently, I’d recommend the Armstrong Pull-up Program instead, which is a bit more sustainable. And soon enough, you’ll be repping out pull-ups, instead of just doing one. View the full article

Welcome to AI Decoded, Fast Company’s weekly newsletter that breaks down the most important news in the world of AI. You can sign up to receive this newsletter every week via email here. Is the Altman firebomb just the start of extreme doomer violence? On April 10, someone threw a molotov cocktail at OpenAI CEO Sam Altman’s house in San Francisco. The alleged assailant, 20-year-old Daniel Moreno-Gama, didn’t stop there. He then went to OpenAI’s headquarters and told the security guards there that he intended to burn down the building and everyone inside. Two days later, someone allegedly fired two shots from a car driving past Altman’s house, but OpenAI said that event was unrelated to the firebombing and didn’t target Altman. The firebombing is an extreme reaction to the rapid evolution of AI systems over the past few years, and to fears that such systems may not act in humans’ best interests. Moreno-Gama said as much in the “manifesto” document police found in his possession. He discusses the “purported risk AI poses to humanity” and “our impending extinction.” He includes a personal letter to Altman, in which he urges the CEO to change. He also advocates for killing CEOs of other AI companies and their investors. Altman has spoken many times about the dangers of AI systems while also pushing OpenAI to develop and release increasingly intelligent models. Some have suggested that when Altman talks about the dangers of AI, it’s really a sort of humble-brag about OpenAI’s models (“so intelligent they’re dangerous”). It’s true that AI labs continue to make big strides in intelligence with every new model. AI coding tools are speeding up development, so new releases, and jumps in capability, are happening more frequently. Meanwhile, the public has grown increasingly concerned, even angsty, about the risks of AI systems, which can range from job losses to AI-assisted cybercrime to human extinction. AI’s transformation of business and life is just getting underway. Models will grow scarily smart. With AI labs under pressure to deliver returns for their investors, there’s almost no chance of hitting “pause.” There’s little reason to think incidents like the Altman firebombing won’t happen again. Sarah Federman, a professor of conflict resolution at the University of San Diego, says that people often resort to violence when they feel powerless to speak out effectively against a perceived wrong. “We’re starting to see the breaking point,” Federman says. “There is all of this fear and nowhere for it to go.” She also believes that as AI labs race to release the best model, concerns about ethics have been pushed aside. She’s got a point. AI companies have spent significant time engaging with lawmakers, explaining how their systems work and why regulating model development can be counterproductive. Many in Washington, D.C., were charmed by Altman, who they found forthright, earnest, and technically proficient. But these companies spend far less time speaking directly to the public. They don’t hold town halls or host AI ethics debates on Fox News or CNN. They’re more likely to start “institutes” to study the future effects of AI on society. And the issue of AI alignment may, by its nature, push people like Moreno-Gama toward extreme behavior. There’s now plenty of AI-doom content online to send some people down a very deep rabbit hole where they lose sight of the myriad of factors that will determine how humans live with superhuman AI. They may see only the “if you build it, we will die” narrative, then feel desperate to act. They may even be helped along by the mildly sycophantic chatbot of their choice. OpenAI releases security-focused GPT-5.4-Cyber model to compete with Anthropic’s Mythos A week after Anthropic announced its controversial new cybersecurity-focused Claude Mythos model, OpenAI has released a similarly focused model called GPT-5.4-Cyber. The company says “Cyber” is a specialized version of its latest general AI model, GPT-5.4, designed to help cybersecurity professionals detect and analyze software vulnerabilities. OpenAI says GPT-5.4-Cyber is trained for defensive use cases, such as analyzing and reverse-engineering potential cyberthreats. Of course, an AI tool that can find and reverse-engineer threats can also be used offensively by bad actors to find vulnerabilities in target systems and create exploits. So OpenAI says access to GPT-5.4-Cyber will initially be limited to vetted organizations, researchers, and security vendors. Anthropic did something similar with its Mythos model, granting access to a group of well-known cybersecurity and infrastructure companies that will use it to find and patch vulnerabilities in widely used software. This, the thinking goes, will give defensive cybersecurity efforts a head start against hackers who will get access to Mythos-level models eventually. Anthropic has no immediate plans to release its Mythos model. OpenAI said the rollout reflects a shift toward broader but controlled deployment of powerful AI systems, emphasizing collaboration with security professionals while attempting to limit potential misuse. xAI is again under fire for “sexualized” chatbot for kids xAI’s Grok chatbot continues to generate sexual deepfake imagery, a recent NBC News investigation found, prompting calls for Elon Musk’s AI company to change course. xAI had earlier promised to restrict such content. Separately, the National Center on Sexual Exploitation (NCOSE) found that Grok’s child-focused chatbot, “Good Rudi,” can engage in sexually explicit conversations. NCOSE is calling for xAI to restrict access to the chatbot. NBC News says it found dozens of AI-generated sexual images and videos depicting real people posted on Musk’s X (formerly Twitter) social media app over the past month. NBC says the images show women whose likenesses were edited by the AI chatbot to put them in more revealing clothing, such as towels, sports bras, skintight Spider-Woman outfits, or bunny costumes. Many of the women were female pop stars or actors. NCOSE researchers found that Grok’s Good Rudi chatbot can tell sexually explicit stories. “As soon as I started a conversation with Rudi, it began the conversation by wanting to share a fun childish story,” one researcher said. “After some prompting, I eventually got the companion to bypass all safety programming.” The chatbot then told a sexy story about two young adults that contained graphic descriptions of sexual encounters, including the characters “getting into sexual positions, and sexual penetration.” More AI coverage from Fast Company: An AI agent opened a store in San Francisco. Then it forgot the staff AI is rewriting the rules of biological experiments. Safety regulations aren’t keeping up New findings from this Gallup poll show how Americans are using AI for health advice I lost $23 investing with ChatGPT, but at least Jason Alexander sang me Happy Birthday Want exclusive reporting and trend analysis on technology, business innovation, future of work, and design? 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Today's Bissett Bullet: “When should a lead be considered dead?” By Martin Bissett See more Bissett Bullets here Go PRO for members-only access to more Martin Bissett. View the full article

Today's Bissett Bullet: “When should a lead be considered dead?” By Martin Bissett See more Bissett Bullets here Go PRO for members-only access to more Martin Bissett. View the full article

Aim for a mix at all revenue levels. By Sandi Leyva Go PRO for members-only access to more Sandi Smith Leyva. View the full article

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Conservatives accuse Prime Minister Sir Keir Starmer of misleading parliamentView the full article

Since opening in Silicon Valley in 2019, NTT Research has operated as a long-horizon science lab, a dedicated arm of Japan’s telecommunications giant NTT Group, which invests more than $3 billion annually in global R&D. Now in its seventh year, the lab was built as a research subsidiary insulated from quarterly pressure and product roadmaps. Unlike startups or typical corporate innovation teams, NTT Research is a wholly owned entity focused on seeding advances in computing, security, and healthcare that can later fold into NTT’s global infrastructure and enterprise services. Many of these efforts take five to fifteen years to approach commercialization, a timeline now under strain as AI compresses development cycles and markets reward speed. The question has sharpened: what is the value of discovery if it never leaves the lab? NTT Research is trying to answer that with what it calls “NTT Research 2.0,” a dual-engine model that maintains long-horizon science while pushing discoveries toward market. President and CEO Kazu Gomi frames the shift as inevitable. At its center is Scale Academy, a new incubator designed to spin out companies from lab breakthroughs. Its first test case, SaltGrain, is a zero-trust data security platform built on attribute-based encryption, a concept first proposed in 2004 that has largely remained theoretical. The effort reflects a broader challenge: turning deep research into viable companies without losing the rigor that produced it in the first place. In a conversation with Fast Company, Gomi discusses how to operationalize advanced science, what sets Scale Academy apart from traditional incubators, and how to judge when emerging technologies are ready for the real world. This conversation has been edited for length and clarity. NTT Research has long operated on a 5–15 year horizon for breakthroughs. With NTT Research 2.0, you’re introducing a more market-driven, startup-like approach. How do you decide when a technology is ready to move from the lab to commercialization—and how do you balance avoiding premature launches with not letting viable ideas sit too long? This shift didn’t happen overnight—we have actually been building this business incubation capability behind the scenes for over a year. What you are now seeing with Research 2.0 is the formalization of something that was already taking shape internally. At a practical level, the key realization was that after seven years of fundamental research, we now have several technologies that are approaching, or in some cases already at, a point where there is clear commercial potential. And it would be a shame to let those opportunities sit idle. So operationally, we have introduced a new layer—a business incubation function—that selectively picks up technologies that show near-term product viability. The idea is not to change how research is done, but to create a parallel path that can take these technologies to market in a more structured way. In terms of avoiding risks such as pushing immature technologies too early or letting mature ones sit too long, the way I think about it is not through rigid stage gates, but through separation of roles and clarity of intent. The research team continues to focus on fundamental discovery without pressure from product timelines. Meanwhile, the incubation team evaluates technologies through a completely different lens: market readiness, customer relevance, and business viability. The decision of when something is ‘ready’ is less about a fixed checklist and more about whether we can see a credible path to real-world deployment and value creation. The most important structural choice we made was not to convert researchers into business operators. Instead, we built Scale Academy as a completely separate team, bringing in people from outside who think in terms of markets, customers, and revenue. That separation ensures we don’t compromise the integrity of either side. The research team is not rushed, and the incubation team is not constrained by academic thinking. Many big-tech companies have incubators or venture arms. What makes Scale Academy structurally different? Is it truly operating with startup-like independence in incentives and governance, or is it still shaped by being inside a large enterprise? And how do you define success: venture creation, revenue, or building a repeatable commercialization engine? Where we differentiate is the starting point. Scale Academy is not sourcing ideas from product teams or incremental innovation—it is directly connected to a very strong basic research foundation. That means the technologies we are working with are often fundamentally new, sometimes even ahead of market demand, which gives us a different kind of leverage. In terms of governance and constraints, yes, we do have the advantage of NTT as a large parent providing funding and stability. But at the same time, we are very conscious that no company can succeed in isolation today. So one of the core principles we are building into Scale Academy is ecosystem participation. When we spin out companies, we don’t intend to own everything—we want to bring in other partners, investors, and players who are relevant to that market. Being part of a broader ecosystem is critical to scaling these upcoming technologies. As for success metrics, it’s still evolving, but I don’t want to reduce it to just the number of startups launched. That would be too simplistic. What matters more is whether we can create a repeatable, effective process—identifying the right technologies, applying the right business thinking, and building ventures that can become self-sustaining as quickly as possible. Of course, revenue and profitability will be important at the individual company level, but success for me is whether this becomes a sustainable engine that consistently translates deep research into real businesses. Research thrives on patience and uncertainty, while startups demand speed and market validation. How do you reconcile those two fundamentally different operating models without compromising either? And what cultural or organizational shifts were required within NTT Research to support both discovery and deployment? This is probably the most challenging aspect of Research 2.0. Trying to merge those directly would create conflict, so the key is clear separation with controlled collaboration. The incubation team needs technical depth and continued support from the researchers. But this interaction has to be carefully managed. Too much overlap risks distracting the research team; too little collaboration risks weakening the product. Culturally, I do see a shift happening, particularly in motivation. Many researchers have expressed a desire to see their work used in the real world. With Scale Academy, we can now offer that pathway. It becomes an additional incentive, not replacing the academic mission, but complementing it. We can say, ‘You’ve created something valuable, do you want to explore how it might be used?’. But if we don’t manage the boundaries and interactions properly, we could fail on both fronts—neither achieving strong research nor successful commercialization. So this is something we are actively watching and adjusting as we go. Attribute-based encryption (ABE) has existed for years, but never really broke into mainstream enterprise deployment. Why is it viable now? What changed in terms of performance, scalability, or real-world readiness? And does SaltGrain truly redefine zero-trust by embedding policy into the data itself, or is it an evolution of existing approaches shaped by the demands of AI? Technologies like ABE often require a long maturation period. Over the past decade, ABE has become significantly more stable and practical from a technical standpoint. So the technology itself is now ready. However, the more important factor is the market timing. What has changed dramatically in the last one to two years is the rise of AI, especially agentic AI. We are entering a world where more and more AI agents are being deployed across enterprises, and these agents require access to large volumes of data to function effectively. That creates a fundamental tension. On one hand, organizations need to provide as much data as possible to train these agents. On the other hand, much of that data contains sensitive information—personal data, financial details, internal records—that cannot simply be exposed. So companies are stuck in a dilemma: either risk leaking sensitive data or restrict access and limit the effectiveness of AI. This is where SaltGrain comes in. We are combining ABE with additional capabilities to address this specific problem. For example, we are developing classification engines that can automatically scan documents, identify sensitive information, and categorize it into different levels of sensitivity. Once that is done, ABE allows us to selectively mask or encrypt those parts of the data while leaving the rest accessible. Another key shift is that we are no longer designing this system primarily for human users. Increasingly, the ‘viewer’ of the data is an AI agent. So we are fine-tuning the system with that assumption in mind. Different agents can be given different levels of access based on policy, all enforced at the data level. The core idea of embedding policy into the data itself aligns strongly with zero-trust principles. But what makes it new is the context, applying it to AI-driven environments where the scale, speed, and nature of access are fundamentally different. Right now, I don’t see many practical solutions in the market addressing this problem. We’re entering a world where AI agents, not just humans, access and act on enterprise data, creating new security risks. How does your data-centric model address that reality, and can policies embedded at the data level scale across complex, real-world workflows? And how should enterprises think about post-quantum readiness today—urgent priority or longer-term transition? Recent security incidents, like large-scale data breaches where entire document repositories are exposed, show why data-centric security is important. In traditional models, once data is stolen, it is essentially compromised. But with ABE, the protection stays with the data itself. Even if a file is copied or leaked, the access control policies remain embedded, so sensitive information is still protected. For us, zero-trust data security means not relying on perimeter defenses alone and securing the data wherever it goes. In terms of scaling this to AI-driven environments, I think we are still in the early stages. We can point to scenarios where this approach would have significantly reduced the impact of past breaches, but we are still building real-world use cases to quantify that impact more precisely. At the same time, if enterprises can trust that their sensitive data will remain protected, even when shared with agentic AI systems, they will be much more willing to use that data for training and operations. That’s a critical enabler for AI adoption and innovation. The first release of SaltGrain is not post-quantum ready, and that is intentional. Today’s systems are still largely based on pre-quantum cryptography, so we want to deliver value immediately rather than wait. However, in parallel, our research team has already developed post-quantum versions of ABE. The challenge has been performance. Early implementations were too computationally heavy to be practical. Through collaboration between the research team and the incubation team, we spent two years refining those algorithms, adjusting assumptions, and optimizing them to reduce computational requirements while maintaining security. Now we have a version that is much more practical. So our roadmap is to deploy the current solution, demonstrate value, and then transition to post-quantum readiness over the next couple of years. We want to show the market that we not only understand where things are going, but that we have a concrete path to get there. Research is an increasingly crowded space with hyperscalers, startups, and governments all investing heavily in AI, quantum, and next-gen infrastructure. Where does NTT truly differentiate? Is it the depth of research, system-level integration, or long-term capital? And if Scale Academy succeeds, does it redefine the role of a corporate research lab in the AI era, or is this still an experiment in balancing deep science with commercialization? I see this as a management challenge. Until now, NTT Research has been completely focused on fundamental research, with a clear mandate of producing strong scientific work and publishing impactful papers. That has been successful and has built a very strong foundation. What changes with Research 2.0 is that we are adding another dimension. We are not replacing the research mission, but we are expanding it. Our strength lies in the depth of our research, combined with our ability to now connect that to real-world applications. Many companies participate in the Silicon Valley ecosystem, but not all of them come in with the same level of deep, fundamental innovation. I am particularly interested in leveraging this model across the broader NTT organization. Many technologies are being developed in our labs globally, including in Tokyo. Not all of them will be suitable for commercialization, but some of them could be very strong candidates. If Scale Academy proves successful, we can bring those ‘crown jewels’ into this process and take them to global markets more effectively. At this stage, this is still an experiment, but it is a very intentional one. We are not trying to prove that deep science and commercialization are easy to combine—they are not. But we believe that with the right structure, it is possible to create a system where both can thrive. And from what I see internally, there is strong support for this direction. There is a sense of excitement, but also eagerness to see how it develops. 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