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New data from AlsoAsked put AI-generated people also ask in Google Search at 38%. These are people also ask responses labeled as AI generated and that number jumped from 17.8% just a few months ago.View the full article

Google added Nano Banana Gemini image features to Google Search through Google Lens. There is a new option named Create mode that unlocks the Nano Banana feature.View the full article

Google has released version 22 of the Google Ads API, this is a major release with dozens of updates. Updates include smart bidding exploration expansion, improvements to DemandGen, PMax campaigns, App campaigns for installs, and more.View the full article

Google has updated the list of Google products and services that use the Google Read Aloud user agent. The updated page also says that the "service provides an audio version of web pages by understanding their content through help of AI" but adds, "no data from the page is retained for AI model training."View the full article

The CPS accused of taking the ‘nuclear option’ after three witness statements that it says left a hole in its caseView the full article

We analyzed 1,000 domains to uncover how backlinks influence AI-generated answers. The verdict: quality and authority drive visibility in AI search. View the full article

The UK’s rules for secondary listings on the London Stock Exchange could drive British business abroadView the full article

Every day another industry leader proclaims that everything will change with AI. While there is no question AI is the most transformative tech shift since the industrial revolution, all the hype means leaders lack real answers about how those changes will roll out or improve critical decisions that will impact the future of their business. As the CEO of a technology company that has invested over $2 billion in evolving our cloud and managed services platform over the past 14 years, I have seen firsthand how foundational innovation sets the stage for transformational leaps. Two years ago, we recognized that AI had matured from future potential to strategic imperative—prompting a $100M investment while continuing to evolve our existing platform. Soon we will launch the next generation of our platform with AI fully integrated, putting our customers in a winning position while giving our internal teams the early-adopter advantage to evaluate vendor solutions and empower employees to harness AI’s full potential. Our team sees AI following the 80/20 rule: 80% of jobs will change 20%, and 20% of jobs will change 80%. Here are four questions we continue to ask as we progress on our AI journey, for our customers and internally: 1. How are we encouraging AI literacy across the entire organization? AI is moving too fast to guarantee ROI; but it is too transformative to gatekeep it from employees. You will never find out how AI can help your teams unless you enable innovation that comes from all levels in the organization. The best way to encourage that is by giving tools to everyone. In our case we added Microsoft CoPilot to our existing contract as soon as it was released to encourage team members to use AI in their daily work while running AI literacy and education programs across the company. 2. What is the AI innovation process? As a leader, you want to operationalize curiosity and let every employee explore, test, and learn what is possible with AI. The promise of AI is that a non-coder can build an app, and it is true. Your most innovative team members will build their apps for their business problem and go fast—and if you offer a little help, these innovators will go faster. In the last six months we have seen 725 exploratory agents created by employees experimenting with CoPilot. Of those, 40 agents have been selected to scale and are being used routinely across teams. Meanwhile, IT continues to offer technical and moral support to the self-starters. The most important part of this process is understanding these employee-led innovations and applying them to the broader organization. In our case, promising ideas are funneled through a formal review process to assess their potential for enterprise-wide scalability—for instance, a services-built app was expanded with enterprise support to also support sales. When a use case requires IT or development resources, the innovator helps define the use case and provide an ROI, after which the AI steering committee prioritizes initiatives based on their potential impact. This ensures the most valuable ideas move forward efficiently while the entire organization learns. 3. What is the A2A and MCP story? As a CEO you need to learn the key terms A2A and MCP and push for these standards to avoid your teams building silos of technology that become obsolete as they cannot work with other systems—the bane of every enterprise back office. A2A is agent to agent; think of it as APIs for agentic. One vendor’s agent speaks to another vendor’s agent, which allows the passing of context so that an enterprise workflow can succeed. Vendors will resist this as they try to monopolize your spend. MCP is model context protocol. MCP is a server that sits in front of a data repository and summarizes the context for agents. This reduces the need for agents to access your data—which in many cases they do not need; they only need the context of a situation to decide what action to take. This will keep your teams from getting caught in a quagmire of creating data silos while reducing your need for data warehouses. 4. When will we see the ROI? In the rigid world of IT timelines and budgets, we would all like to see ROI immediately. But remember the greatest innovations and paybacks often come from learning and failure. After all, when asked about all the missteps on his way to create the electric light, Edison famously answered: “I have not failed. I’ve just found 10,000 ways that won’t work.” Artificial intelligence is going to change the world and everything that we do. Failure to invest in AI will put your entire business at risk, and a short-term focus on ROI is myopic. Our culture is about “better, better, never best,” where we celebrate success and learn from failure. This approach has enabled our product teams to evolve our platform at the right time for artificial intelligence, and for our IT organization to draft on those learnings while creating an environment where employees can innovate and keep Calix and our customers as leaders of the AI opportunity. View the full article

There’s no shortage of challenges facing employers and the U.S. workforce. From economic concerns to the impact of AI, both workers and organizational leaders are navigating big changes. One trend deserves particular attention: working mothers are reevaluating their place in the workforce. As reported by the Washington Post, the share of mothers aged 25 to 44 with young children who are in the workforce is on the decline, reaching its lowest level in more than three years. This shift has direct implications for recruiting, retention, and overall market competitiveness. But it also opens the door for leaders to make a meaningful difference for their employees. Understanding the pressures working mothers face Research by Harris Poll and KinderCare confirms this same reality: Working parents are balancing tremendous responsibilities at home and at work. As a working mother myself, I know firsthand how real these pressures feel. At the heart of the challenge is support—or too often, the lack of it. Our 2025 Parent Confidence Index, a survey of 2,000 U.S. parents with children under age 12 conducted with the Harris Poll, found that working parents are especially impacted by back-to-office mandates. Nearly three-quarters of parents are now working in-office full-time or in hybrid roles, and 60% say this has impacted their child care needs. Many would still prefer remote arrangements: 40% said “all remote” is their ideal, and nearly half felt pressured to return to the office. As employers continue to evolve their workplace policies, what working parents want from employers is clear. More than three-quarters believe employers should offset the cost of child care. Parents told us that they specifically want subsidized, on-demand, and on-site child care options, depending on their working scenario. Yet there’s often a perception gap. While nearly half of all employees say they want child care benefits, only a third of chief HR officers (CHROs) believe their workforce needs them. And a striking 60% of employees say they’d rather have child care subsidies than a raise. Child care isn’t just a “nice-to-have” when nearly three-quarters of parents say it would be impossible to do their job without reliable, high-quality care. Among those who already have benefits, 90% report that quality child care gives them peace of mind to perform well at work. Employers can be a part of the solution While many parents still say finding child care feels challenging, employers are uniquely positioned to be part of the solution. Child care benefits don’t just support families—they strengthen business outcomes: Employee performance: Nearly 60% of parents say unreliable child care has hurt workplace performance. Retention: More than half would stay at a job because of child care benefits, and one-third or more would switch to get them. Reputation: More than 80% of employees believe how a company supports parents reflects how it cares for employees overall. Employers who lead here will be seen as family-oriented, caring, empathetic, and forward-thinking. A practical path forward The good news is that solutions are accessible and effective. Child care programs are not difficult or cost-prohibitive to implement, and their return on investment is significant when compared with the high cost of turnover (currently estimated at 50% to 200% of the employee’s annual salary). For example, Thomas Jefferson University and Jefferson Health partnered with KinderCare to assess how family care challenges affected their workforce. The survey revealed a clear ROI opportunity: Access to reliable child care would significantly reduce absenteeism, a critical issue in healthcare. The straightforward solution was to open an on-site child care center. KinderCare helped bring that vision to life, which continues to thrive, providing essential support to their working parents. Talent isn’t disposable. It is the organization’s margin of difference. Ultimately, the message for C-suite and talent leaders is simple: listen. More than 80% of employees who are parents believe that how a company supports its working parents reflects how it cares for its employees overall. Employees are telling us what matters most—supporting the integration of home and work. By developing programs that speak directly to workforce realities, organizations strengthen retention, productivity, and competitive position. View the full article

Result marks opportunity to pass budget for 2026 in deeply fractured parliamentView the full article

If you hear your organization talking about the Great ShakeOut, it has nothing to do with Taylor Swift or Florence and the Machine. Instead, this international event promotes earthquake preparedness. Having a plan greatly improves outcomes and saves lives. On October 16 at 10:16 a.m. local time, millions will be practicing how to properly drop, cover, and hold on. Let’s take a look at the science behind earthquakes, the regions they impact, and how to participate in the Great ShakeOut. What actually causes an earthquake? The Earth’s outer layer is made up of seven major tectonic plates. Think of these as patches of a quilt that isn’t stitched together perfectly. The places where the plates meet are called plate boundaries. Some of these contain fault lines. The patches or plates move since they are not properly connected, which causes stress to build up at the borders. When this reaches a boiling point, the pressure is released, causing the earth to shake. Which regions have the greatest earthquake risk? According to the United States Geological Survey, 81% of earthquakes take place along the Circum-Pacific seismic belt, which is located on the rim of the Pacific Ocean. This shaky area is also known as the Ring of Fire because of its plethora of volcanoes. Because of Japan’s advanced ability to detect earthquakes, it holds the dubious honor of having the most recorded quakes, though the USGS says it’s more likely that Indonesia experiences the most quakes annually by virtue of its larger size. The most catastrophic earthquakes have tended to occur in China, Iran, and Turkey. How can I participate? Organizations and individuals are welcome to participate in the Great ShakeOut. You can even make it a family affair. Register at shakeout.org to make sure your efforts are counted. There, you’ll find resources such as a drill narration and discussion questions for a post-event debrief. There are also steps to take to be ready for “the big one.” These include making sure furniture and decorative items are secure, having a disaster plan, and keeping emergency supply kits stocked and up to date with all the necessary items. View the full article

America’s use of import duties has been constrained by financial markets and economic realityView the full article

The whole idea of advertising—using pictures and words to get people to buy stuff, or to do something—is old indeed, with the first known example dating back almost 5,000 years to the heady days of Ancient Egypt. The ads business changed a lot since we were writing notices on papyrus, but one thing that—until recently—remained the same was that it was a deeply intentional business. The advertiser had to think about the language they used, the imagery they employed, the types of people they sought to reach, and how they would go about doing that. Whether the advertiser was touting a weaving shop on the banks of the Nile during the days of the Pharaohs, or selling detergent or cigarettes through new mass media innovations like the television or the radio, that same thoughtfulness was a constant. Advertisers had to think—and, by virtue of the fact they were forced to make decisions, they were in control of everything. My biggest complaint with the digital ads ecosystem is it, by design, strips the ability of the advertiser to make some of those decisions—not merely placement, but targeting, and with the emergence of dynamic creative and generative AI, messaging too. In the process, we’ve turned advertising from a very deliberately engineered system—where the architect knows what each part of the process should do—into one that’s, essentially, a black box. And within this black box, there’s little room for creativity. The Process Is Creative When we think about advertising—and, in particular, good advertising—we always think about the messaging. It’s true that some of the best campaigns in history have always used clever wordplay, or coy psychological tactics, to drive a point home. The Pepsi Challenge, for example, started off as a series of in-person taste tests and culminated in a campaign that could confidently say (though some have identified flaws in the test itself) that Pepsi was America’s preferred cola. Not only did this directly undercut Pepsi’s main adversary—Coca-Cola, which easily had the most powerful brand perception—but it also allowed people to differentiate between products that people might otherwise think of as identical. Messaging is important, but it’s far from the only creative part of the marketing process. Take Subaru, for example. In the 1990s, it was a struggling car brand—eclipsed not only by its Japanese rivals like Nissan and Toyota, but also by fierce domestic competition in the U.S. market. Subaru hired a new advertising firm to turn its fortunes around, which ran a series of focus groups that asked why existing Subaru owners chose its vehicles, as opposed to those from one of its healthier rivals. That firm noticed that women dominated those focus groups, and many of those women identified as lesbian. The company then launched a campaign that targeted both women and lesbians—itself a brave choice, considering the climate of the 1990s, which saw the passage of both the Defense of Marriage Act and Don’t Ask, Don’t Tell. To help it reach lesbian audiences, it hired Mulryan/Nash to create content specifically for the gay press, and to handle ad buying. This campaign wasn’t just pioneering—it also, arguably, helped revive Subaru’s fortunes, and the brand remains vibrant and relevant, especially in the U.S., where it sold over 667,000 cars in the 2024 calendar year. The Subaru example is a potent one, not simply because it was so successful, but because it illustrates how each step of the process—from identifying the customer, to determining where to reach them, to crafting the messaging—required human thought and human creativity. If we’re looking for a more contemporary example, Spotify’s controversial “Thanks 2016, it’s been weird” springs to mind. Capitalizing on a year defined by seismic political shifts, celebrity deaths, and countless surreal moments to mention in the confines of this piece, Spotify tapped into its data, identifying equally surreal trends and turning them into highly relatable billboards positioned in prime urban locations. These billboards featured pithy one-liners (for example, “Dear person who played ‘Sorry’ 42 times on Valentine’s Day, what did you do?”), with the text localized for target markets (“Dear 3,749 people who streamed ‘It’s the end of the world as we know it’ the day of the Brexit vote, hang in there”). It served as a reminder of how music isn’t simply a form of entertainment, but a way in which we process events in our personal lives, as well as those happening within politics and culture. Again, this process required creative thinking at every level—from identifying the patterns within the data that would lead to the funniest trends, to choosing the most valuable locations to place the billboards. I write all of this not because I believe that all digital advertising—where these decisions are outsourced, particularly to third-parties—is bad, but because I believe that the most effective and memorable campaigns are thoughtful ones. The reason why I believe digital advertising is the enemy to advertising is because, by design, it strips us of the ability to use that creativity across all stages of the advertising process, from conceptualization to creating the final product. Battling the Black Boxes Last year was the 30th birthday of digital advertising. It’s interesting to see how, as the internet grew and an adtech ecosystem emerged, the very nature of how this segment actually works changed. Whereas at one point advertising deals were inked between companies, with money changing hands in exchange for prime placement for a set number of days, those manual transactions are now a thing of the past. Today’s digital advertising mechanics are based on systems which the advertiser doesn’t control or even understand—and in the case of those which heavily rely on AI, even the developers don’t have full insight into the factors behind each targeting and placement decision. This opacity also allows the adtech provider or advertising network to act in ways that are contrary to the interests of the advertiser—either by obfuscating data that could allow them to make more effective decisions, or by failing to protect said advertiser from, for example, click fraud. Although digital ads allow a company to target and market at scale—and, arguably, with the economies of scale that wouldn’t be otherwise possible—the downside is, arguably, a degradation of the online experience for end users, profound concerns about user privacy, and an absence of transparency for those actually purchasing the ads. Arguably, the biggest downside—from someone who cares profoundly about the intellectual and creative brilliance of the ads industry—is that digital ads haven’t really produced something that’s memorable, or has had any meaningful cultural impact. Coca-Cola gave us Santa Claus’s red outfit and the iconic flashing delivery trucks. Decades after they commercials first aired, we still remember the Budweiser frogs croaking “bud-wise-er”, or its later ads that turned “wazzup” into a legitimate pop culture phenomenon (albeit a really irritating one). And that’s because creativity is like a muscle, and if you don’t exercise it—or don’t have to exercise it—it’ll wither away. View the full article

The Interborough Express line—the long-awaited light-rail link between Brooklyn and Queens in New York City—hasn’t broken ground yet. But on my computer screen, one part of the route is already operational. A new simulation game called Subway Builder lets you design, build, and operate subway systems in 26 U.S. cities, from New York to Boston to San Francisco. The game uses real-life U.S. Census Bureau and employment data to map where residents and workers live, allowing you to simulate realistic passenger flows. Players must also contend with real-world constraints like tunnels, viaducts, existing foundations, and road layouts. The goal is to design a subway network that gets the most people to their destinations as quickly as possible. But there’s a deeper ambition: to spark more transit-minded thinking in a country that historically has underinvested in it. “I would secretly hope that maybe someone in power sees this and says ‘Maybe we can build something like this,’” says Colin Miller, a software engineer and creator of the game. Building a hyperrealistic transit game Subway Builder launched on October 9 to much fanfare in the transit community. “I’ve been playing Subway Builder for *checks notes* all night,” one user posted. “I legitimately think this game is going to start a transit revolution in America,” wrote another. Over the years, many developers have tried to gamify transit design with offerings like MetroConnect, Brand New Subway, and Mini Metro. But few have attempted to make their simulations realistic enough to replicate real transit-planning challenges at the scale of cities like New York or Seattle. To create Subway Builder, Miller drew on datasets from the U.S. Department of Education, the Federal Aviation Administration, and OpenStreetMap, among others. You can analyze demand statistics on a map of your chosen city; then, once you build a route, explore ridership station by station. One of the most satisfying features for me remains the constellation of red dots that represent riders commuting toward newly built stations and journeying across a network I just built. The cost of building public transit in the U.S. Subway Builder bills itself as hyperrealistic, but there are two key exceptions: politics and budgeting. Miller says he did not take into account the political will in any given U.S. city, nor did he calibrate the game’s budget to U.S. infrastructure costs. Instead, he used Spanish construction costs, which are among the lowest in the world. (Madrid, for example, tripled its metro network in just 12 years.) “If I had it set to realistic American construction prices, it would have made the game unplayable because you’d run out of money,” he concedes. Players can choose to play in “sandbox mode,” which comes with no budgetary constraints. It’s the game’s “normal mode” that reveals a painful fact long criticized by experts—namely that building transit in the U.S. is mind-bogglingly expensive. On average, domestic rail transit projects cost roughly twice as much per mile in the U.S. as they do in Europe or Canada, and as much as five times more in New York City. The relatively recent Second Avenue Subway expansion, for example, cost about $2.5 billion per mile. For reference, the Los Angeles Purple Line extension cost $800 million per mile, while Madrid’s extension was $320 million per mile. When I played the game, I quickly learned that even $3 billion would get me only three lines and about 20 stations in Brooklyn. I also learned that building a subway route is just the beginning of a long road plagued by never-ending signal failures, broken-down trains, and overall operational costs. And considering that every dollar collected from fares helps fund new routes and buy new trains, I gained a bit more sympathy for the MTA’s recent war on fare evasion. A tool for publication imagination Much ink has already been spilled on the state of mass transit in the United States. Transit advocates such as Yonah Freemark have frequently lamented declining ridership and funding shortfalls in American cities. Others, like Brent Toderian, have emphasized the role of transit in shaping equitable, walkable urban environments. While public transit has recently blossomed in many U.S. cities, the system remains plagued by some of the world’s highest construction costs, red tape, political fragmentation, and a misguided adulation for the freedom that cars provide for the benefit of the few at the expense of the many. Hayden Clarkin, a transport engineer and planner who recently published a “Hitchhiker’s Guide to Building a Lot of Subways,” argues the U.S. has the ability to build a world-class transit system but lacks the will. “Imagine what we could achieve if we built up our institutional capacity and if leaders spent as much political capital on transit as they do on expanding highways,” he told me via email. “The systems other G7 nations have enjoyed for decades are not beyond our reach—they are a choice we can and must make.” For Clarkin, games like Subway Builder aren’t just entertainment. He believes they could actually have real-world impact. “This is a tool for public imagination,” he says. “I’m genuinely excited for the day someone takes their in-game map to a city council meeting and says, ‘Look at what we can achieve!’” View the full article

To announce its entrance into 5G home internet service, Mint Mobile found the real-life version of a new AI-generated actress, even if only in (nick)name. Tilly Norwood is the name of a so-called AI actress launched by AI talent studio Xicoia. It also happens to be the name of a woman who stars alongside Ryan Reynolds in Mint Mobile’s new ad for its home internet service, which it’s branding “Minternet.” “It’s hard to believe that Mint is launching 5G home internet. It’s also hard to believe that a real version of an AI actress is out there,” a Maximum Effort representative tells Fast Company. “And thanks to the incredible and somewhat disturbing stalking detective abilities of our team, we found her. Just outside of Dallas, Texas, just one day before filming the commercial. Luckily she responded to our random DMs and was happy to assure the world that both she and the internet are very real.” The fake Norwood has inspired backlash and a Wikipedia page, and the labor union SAG-AFTRA refused to say in a statement that the AI character is an actual actor, instead stating it’s “a character generated by a computer program that was trained on the work of countless professional performers—without permission or compensation.” As it happens, the real Norwood—Natalie “Tilly” Norwood—is a real Mint Mobile customer. In the commercial, Reynolds, a former Mint Mobile co-owner who still makes ads for the wireless service provider through Maximum Effort, a production company he cofounded, asks if Norwood is real and “not an AI-generated combination of actors.” “I’m a combination of my parents,” the real Norwood says. Mint Mobile’s parent company was acquired by T-Mobile in 2023 in a deal worth up to $1.35 billion, and its 5G home internet service shows the brand is broadening its ambitions beyond mobile. The brand says its home internet service will use T-Mobile’s 5G network, and Mint Mobile is offering it for as low as $30 a month for customers with a Mint Mobile phone plan who prepay for three months. In an advertising landscape that could increasingly see more AI-generated ads, sticking to real people is a smart strategy. A 2024 YouGov poll of respondents from 17 markets around the world found 51% were uncomfortable with a brand creating a virtual ambassador (34% were comfortable with it; 15% didn’t know how they felt about it). In other words, using a fake Tilly Norwood in your ad could turn away half your audience. Meanwhile, the real living, breathing Tilly Norwood appears to be anything but polarizing. View the full article

Newly released witness statements leave Crown Prosecution Service facing questions about why it didn’t press ahead View the full article

Endorsement comes after Berlin signals readiness to allow more centralised markets supervision View the full article

The J.M. Smucker Co. says it doesn’t have a problem with other companies selling their own prepackaged, crustless sandwiches like its own popular Smucker’s Uncrustables. They just have to get their own design. Uncrustables is on its way to becoming a $1 billion brand, so of course there will be knockoffs, but according to Smucker, a recent Trader Joe’s version of Crustless Peanut Butter & Strawberry Jam Sandwiches is a bit too blatant. The company is using the design of the Trader Joe’s product and packaging to prove its point in a new lawsuit. Smucker accused the grocery store chain of “an obvious attempt to trade off of the fame and recognition” of Uncrustable’s protected design marks in a suit filed Monday in the U.S. District Court for the Northern District of Ohio. The round shape and crimped edges of Trader Joe’s crustless sandwiches, which it released in late summer, look too similar to Uncrustables, Smucker says. “Smucker does not take issue with others in the marketplace selling prepackaged, frozen, thaw-and-eat crustless sandwiches,” attorneys for the Orrville, Ohio-based food and beverage manufacturer wrote in the suit. “But it cannot allow others to use Smucker’s valuable intellectual property to make such sales.” Smucker, which reported annual net sales of $8.7 billion in the most recent quarter, says it has invested nearly $1 billion over 20 years to grow its brand of crustless sandwiches into the No. 1 frozen handheld brand in its category. It’s paid off even as Smucker’s snack brands like Hostess Twinkies and Ding Dongs, which have recently rebranded, struggled in a shifting snack food landscape. CFO Tucker Marshall said on Smucker’s August earnings call that Uncrustables is a “growth brand” for the company, along with the pet food brands Meow Mix and Milk-Bone. Marshall said that “people who are consuming Uncrustables for the most part are athletes, families with kids,” and that the brand performs strongly at universities and convenience stores. “We really haven’t seen any impact at all from the GLP-1,” Marshall added, referring to weight-loss medications that are driving a trend toward healthier, high-protein snacks. The importance of Smucker’s Uncrustables in the company’s portfolio helps underline the urgency of the lawsuit. In the suit, Smucker argues its trademarks for images like a “pie-like shape with distinct peripheral undulated crimping” as well as “a round crustless sandwich with a bite taken out showing filling on the inside” are being duped by Trader Joe’s without authorization. The suit extends to packaging concerns, as Smucker believes even the blue used for Trader Joe’s box of crustless PB&J sandwiches is strikingly similar to the blue used in the Uncrustables logo. Smucker is seeking damages and demanding that Trader Joe’s destroy all the products, packaging, and promotional materials that use the current designs. Trader Joe’s did not respond to a request for comment. There are other crustless sandwich brands that don’t use the Uncrustables-style circular shape and crimped design, like the square-shaped Jams and Walmart’s Great Value No Crust Sandwich. Chubby Snacks originally launched with circular sandwiches before getting hit with a cease-and-desist from Smucker. It pivoted in 2021 to a cloud-shaped sandwich. Smucker’s suit follows a May lawsuit filed by Mondelez International against Aldi accusing the grocery store chain of duping the packaging of popular snack brands like Oreo and Nutter Butter. Aldi unveiled redesigned private-label packaging in September amid a wider industry trend toward upgrading generic branding that has spanned from Amazon to Walmart. As lawsuits like those from Smucker and Mondelez show, with a rise in private-label competition, the big industry players are ready to protect their own branding, and with teeth if necessary. View the full article

10Web Vibe enables easy WordPress development through spoken interaction The post New WordPress Vibe Coding Simplifies Building Websites appeared first on Search Engine Journal. View the full article

Highest-paying firms increase payouts as the sector’s fortunes reverse, Goldman Sachs report shows View the full article

I was interviewing for a job as a customer service agent with Anna. She had a low, pleasant voice and she’d nailed the pronunciation of my name—something few people do. I wanted to make a good impression except I had no idea what Anna was thinking because Anna couldn’t think. Anna wasn’t technically a person. She was AI. Not only is AI changing how we do our jobs, it’s also changing how we get jobs. This ranges from using AI to screen resumes, schedule interviews, even conduct them. According to a 2025 report, 20% of companies are using AI to interview candidates. Even so, nothing can replace human recruiters, the folks who’ve deployed Anna into the wild stressed to me. After I spoke with her, I quickly understood why. In this story, paid subscribers will learn: What it’s like to actually go through a job interview with an AI agent—and how to speak to them Where companies should deploy AI interviewers that would benefit them and job seekers AI Anna clocks in Even though I wasn’t really interviewing for a job—this was all an exercise for this story, of course—I was still nervous. I asked the team behind Anna to provide a job description so I could prepare, but outside of this experiment, I was sadly lacking in actual customer service experience. I also didn’t know how AI Anna was going to react to awkward silences, panicked misdirection, or if she’d be able to tell if I was lying. These worries are bad enough with a human. How would a computer program react? I got on the phone and connected with Anna. She was pleasant, and frankly, sounded way more human than I was expecting. We exchanged greetings, and before long, I was in full-on job interview mode with an AI. First up, she asked me to describe a time when I had to explain something complex over the phone clearly. I blanked. Finally, I described how journalism involves explaining complex ideas because you’re asking questions. It sounded weak even to my own ears. Sure enough, she was not impressed. “I’d like to explore a scenario that’s more specific to the role we’re discussing,” she replied firmly. Fair point. I managed to dredge something up from a high school job. Mercifully, AI Anna accepted the answer and moved on. Next, AI Anna wanted me to talk about a time when I had to problem solve for a customer. This, I could answer. I dove into my brief stint organizing a literary conference where writers paid to meet with agents. Occasionally agents went astray because they were hungover or running marathons and I’d be left to find alternatives like rescheduling— Anna cut me off. “That sounds like a high-pressure situation. . . . It’s great that you were able to come up with alternatives. Now I’d like to switch topics for a moment.” Yikes. I wasn’t ready to switch topics, but AI Anna was, and I couldn’t tell why. Was my example off topic? Was I taking too long to answer her question? Before I could ask, Anna had already swept on to background checks. I invented a criminal background and told AI Anna I had done some time in prison. She thanked me for being honest, and told me that she could not make any decisions. She said candidates with a criminal record would be considered on a case-by-case basis (something that would have to be verified by a human). Then I wanted to know if I’d be required to work overtime. She let me know I’d be required to do overtime the first six months, but only one or two times a month. Needed, accurate information that couldn’t just be googled—great. Honestly? While I found her transitions a bit jarring sometimes, she handled most questions with aplomb. How we got here AI Anna is the product of PSG Global Solutions, a staffing firm. Before deploying AI Anna in the market, the firm asked Brian Jabarian, a researcher at the University of Chicago Booth School of Business with doctorates in economics and philosophy, to study the AI Anna’s effectiveness. (Jabarian received no funding from PSG). In a study released in September, Jabarian conducted an experiment where 70,000 applicants for a customer service job were randomly assigned a human interviewer, an interview with AI Anna, or the ability to choose between the two. The results are surprising, and surprisingly promising for the candidates. AI interviews resulted in a 12% increase in job offers, and a 17% increase in 30-day retention on the job. Moreover, when offered a choice, 78% of applicants chose to be interviewed by AI. Jabarian theorized this was because the AI was easier to schedule with: job applicants who needed a job quickly could book a call immediately. Why the positive outcome data? Jabarian pointed out that, on average, an AI interviewer got through more required topics than human recruiters since they couldn’t be distracted. (I mean, Anna did move at a brisk clip.) “AI leads to a more consistent interview experience,” he said. “It lets the candidate talk more, and has a 50% chance of covering 10 of 14 required topics compared to 25% for human interviewers.” AI Anna clocks out Afterwards, I debriefed the interview with David Koch, PSG’s chief transformation and innovation officer. First, he showed me AI Anna’s backend: The platform had generated a recording of our conversation, a transcript, a summary of the call (including suggestions for next steps, like a follow up to discuss my criminal background), and an overall recommendation: AI Anna thought I was qualified (yay!) but merited human follow-up because of my criminal background. AI Anna also recommended a follow-up because she’d cut me off when I was talking about the literary conference. Koch explained my speaking cadence is a touch slower than average, and AI Anna is programmed to respond after a certain amount of time or else the flow of conversation can become jerky. Koch noted that AI interviewing was better suited for some situations and not others. He recommended AI interviewing for high-volume hiring where there’s a need to source candidates quickly for jobs that are seasonal and high turnover, like customer service agents or travel nurses. Koch also said AI interviewing is best suited for cases where there’s less complexity, in which you don’t need to sell a candidate on a role. From my standpoint as a lay person, the technology behind AI Anna struck me as marvelous. She corralled me into staying on topic, and was capable of social niceties. She provided detailed answers to all my questions. For recruiters, this could be life changing. It’s not that AI Anna might replace them, per se (there were already things from the interview that a human colleague would have to address or follow up on). But recruiters could farm out tasks like screening calls to AI while they worked on more high-level tasks. However, this made me worry. If AI Anna existed to save companies time, what happens to candidates who get flagged for follow-up, even for something as simple as speaking slowly—let alone a criminal background? If there are more than enough qualified candidates to fill roles, I imagine a harried hiring manager would make offers to people who don’t require follow-up. Exception cases that require more time, like me, might fall to the wayside. The future: cold, but competent After my conversation with AI Anna, I felt hollow. Typically, if an interview goes well, I have the high of having connected with someone who might make me feel valued, desired, and possibly in the mix for a new job. If it doesn’t go well, I spend the next couple of days wallowing in self-pity and dissecting potential red flags. AI Anna’s preprogrammed human-like intonation left me nothing to go on. Did she like me? Or was meh on me, but still think I was qualified? I couldn’t tell probably because AI Anna does not have emotions and did not care about me. But how much does this matter? A Gallup study found that 44% of respondents said their interviews drove them to accept an offer or not. Ideally, candidates would be able to interview with their direct supervisor before getting a job in order to suss out personality match—but for a screening interview, AI Anna’s value was undeniable. She raised the floor for interview quality: She’s personable and she offers a consistent experience. There’s no need to worry about the mysterious intangible of “chemistry.” Jabarian also pointed out that AI interviews reduce gender discrimination by half. Done right, AI interviewers could reduce bias and help qualified job candidates who may not perform well during interviews because they lack intangibles such as charisma. Still, I missed talking to a human. View the full article

Learn what online advertising is, how it works, and the main ad types. Plus, how to launch a campaign. View the full article

Generative AI is evolving along two distinct tracks: on one side, savvy users are building their own retrieval-augmented generation (RAG) pipelines, personal agents, or even small language models (SLMs) tailored to their contexts and data. On the other, the majority are content with “LLMs out of the box”: Open a page, type a query, copy the output, paste it elsewhere. That divide — between builders and consumers — is shaping not only how AI is used but also whether it delivers value at all. The difference is not just individual skill. It’s also organizational. Companies are discovering that there are two categories of AI use: the administrative (summarize a report, draft a memo, produce boilerplate code) and the strategic (deploy agentic systems to automate functions, replace SaaS applications, and transform workflows). The first is incremental. The second is disruptive. But right now, the second is mostly failing. Why 95% of pilots fail The Massachusetts Institute of Technology recently found that 95% of corporate GenAI pilots fail. The reason? Most organizations are avoiding “friction”: They want drop-in replacements that work seamlessly, without confronting the hard questions of data governance, integration, and control. This pattern is consistent with the Gartner Hype Cycle: an initial frenzy of expectations followed by disillusionment as the technology proves more complex, messy, and political than promised. Why are so many projects failing? Because large language models from the big platforms are black boxes. Their training data is opaque, their biases unexplained, their outputs increasingly influenced by hidden incentives. Already, there are companies advertising “SEO for GenAI algorithms” or even “Answer Engine Optimization,” or AEO: optimizing content not for truth, but to game the invisible criteria of a model’s output. The natural endpoint is hallucinations and sponsored answers disguised as objectivity. How will you know if an LLM recommends a product because it’s correct, or because someone paid for it to be recommended? For organizations, that lack of transparency is fatal. You cannot build mission-critical processes on systems whose reasoning is unknowable and whose answers may be monetized without disclosure. From “out of the box” to “personal assistant” The trajectory for savvy users is clear. They are moving from using LLMs as is toward building personal assistants: systems that know their context, remember their preferences, and integrate with their tools. That shift introduces a corporate headache known as shadow AI: employees bringing their own models and agents into the workplace, outside of IT’s control. I argued in a recent piece, “BYOAI is a serious threat to your company,” that shadow AI is the new shadow IT. What happens when a brilliant hire insists on working with her own model, fine-tuned to her workflow? Do you ban it (and risk losing talent) or do you integrate it (and lose control)? What happens when she leaves and takes her personal agent, trained on your company’s data, with her? Who owns that knowledge? Corporate governance was designed for shared software and centralized systems. It was not designed for employees walking around with semiautonomous digital companions trained on proprietary data. SaaS under siege At the same time, companies are beginning to glimpse what comes next: agents that do not just sit alongside software as a service (SaaS); they replace it. With enterprise resource planning systems, you work for the software. With agents, the software works for you. Some companies are already testing the waters. Salesforce is reinventing itself through its Einstein 1 platform, effectively repositioning customer relationship management, or CRM, around agentic workflows. Klarna has announced it will shut down many SaaS providers and replace them with AI. Their first attempt may not succeed, but the direction is unmistakable: Agents are on a collision course with the subscription SaaS model. The key question is whether companies will build these platforms on black boxes they cannot control, or on open, auditable systems. Because the more strategic the use case, the higher the cost of opacity. Open source as the real answer This is why open source matters. If your future platform is an agent that automates workflows, manages sensitive data, and substitutes for your SaaS stack, can you really afford to outsource it to a system you cannot inspect? China provides a telling example. Despite being restricted from importing the most advanced chips, Chinese AI companies, under government pressure, have moved aggressively toward open-source models. The results are striking: They are catching up faster than many expected, precisely because the ecosystem is transparent, collaborative, and auditable. Open source has become their work-around for hardware limits, and also their engine of progress. For Western companies, the lesson is clear. Open source is not just about philosophy. It’s about sovereignty, reliability, and trust. The role of hybrid clouds Of course, there is still the question of where the data lives. Are companies comfortable uploading their proprietary knowledge into someone else’s black-box cloud? For many, the answer will increasingly be no. This is where hybrid cloud architectures become essential: They allow organizations to balance scale with governance, keeping sensitive workloads in environments they control while still accessing broader compute resources when needed. Hybrid approaches are not a panacea, but they are a pragmatic middle ground. They make it possible to experiment with agents, RAGs, and SLMs without surrendering your crown jewels to a black box. The way forward Generative AI is splitting in two directions. For the unsophisticated, it will remain a copy-and-paste tool: useful, incremental, but hardly transformative. For the sophisticated, it’s becoming a personal assistant. And for organizations, potentially, a full substitute for traditional software. But if companies want to make that leap from administrative uses to strategic ones, they must abandon the fantasy that black-box LLMs will carry them there. They won’t. The future of corporate AI belongs to those who insist on transparency, auditability, and sovereignty, which means building on open-source, not proprietary, opacity. Anything else is just renting intelligence you don’t control while your competitors are busy building agents that work for them, not for someone else’s business model. View the full article

Below, Scott Anthony shares five key insights from his new book, Epic Disruptions: 11 Innovations That Shaped Our Modern World. Scott is a clinical professor of strategy at the Tuck School of Business at Dartmouth College. His research and teaching focus on the adaptive challenges of disruptive change. Previously, he spent over 20 years at Innosight, a growth strategy consultancy founded by Harvard Business School professor (and father of the idea of disruptive innovation) Clayton Christensen. What’s the big idea? In 1620, Sir Francis Bacon wrote that there were three technologies for which it was possible to draw a clear line before and after: the printing press, the compass, and gunpowder. Those three technologies that changed the world stretched over 1,600 years. Today, it feels like there’s a big disruptive development every 1,600 seconds. Autonomous vehicles . . . augmented reality . . . artificial intelligence . . . additive manufacturing. And those are just the ones that begin with “A.” How do we make sense of a world where change is truly the only constant? Understanding how disruptive innovation and epic change happens allows us to see the world more clearly. 1. Disruptive innovators transform the world. Florence Nightingale was a nurse. You might have a visual of “The Lady with the Lamp,” and that’s part of Florence’s story, but there is so much more. Shocked by her experience in the Scutari hospital during the Crimean War, she developed a series of analyses, brilliantly visualized in polar area charts that showed the power of prevention and proper hygiene in hospitals. She wrote books explaining the essence of nursing that anyone could buy and read, and set up schools to train nurses. What she did was disruptive innovation. Nightingale enabled a broader population to improve health standards and living conditions, focusing on prevention rather than treatment. Many of the things that we take for granted today, such as modern sewage systems or having light and fresh air during recovery, trace back to Nightingale’s work. Disruptive innovators transform existing markets and create new ones by making the complicated simple and the expensive affordable. They open markets to broader populations that historically lacked wealth or specialized skills. They literally change the world. 2. Every story of disruptive innovation has heroes. In the year 1437, Johannes Gutenberg was working on something in Strasbourg. No, it was not the printing press…at least, not yet. He was part of a team working on a trinket: a mirror that could capture the essence of the Holy Spirit during a planned pilgrimage in 1439. Well, that pilgrimage was called off because of an outbreak of the Bubonic Plague. That was bad for many people, but good for the world, because Gutenberg and his team went in a different direction. They met someone named Conrad Saspatch, who had an innovative wooden press. In 1440, they combined that with a range of other things to create a working version of the printing press. “If you have an idea that you think could be disruptive, you need to find people who will support you.” To commercialize it, they needed customers, scale, and funding. They found a merchant named Johann Fust who gave them the capital to build their business. Fust ultimately sued them and took control of the technology, but that’s not the primary point here. The point is that every story of disruption has a protagonist, but it is always accompanied by multiple people involved. Every story has heroes, and that word is plural. So, if you have an idea that you think could be disruptive, you need to find people who will support you. If you’re in an organization that’s seeking to have more disruptions, you need to make sure the environment supports those innovators who are going to do the work. 3. Disruptive innovation is predictably unpredictable. In 1947, a trio of researchers at Bell Labs developed a breakthrough that would change the world: the transistor. Their goal was to create a technology that would replace vacuum tubes in communications networks. That happened, but the path to get there was unexpected. The transistor was an imperfect product in its early days. It had the benefits of being small, rugged, and not giving off heat, but it was also unreliable. You would have to redesign a system if you were going to use it. It wasn’t good enough to plug into communications networks. The first commercial market was in hearing aids. In 1952, the Sonotone 1010 featured a transistor. The fact that the transistor doesn’t give off heat was a huge benefit for people wearing battery packs on their waists. The fact that it’s rugged was incredibly beneficial. The limitations just didn’t matter. A couple of years later, 95 percent of hearing aids were powered by transistors, and the market had exploded. This is a very predictable pattern. You never know exactly where disruptive innovation is going to start. Generally, however, you know it will be in a place that values it despite its limitations. That place is typically on the fringe of an existing market or in a completely new setting. Around the same time that Sonotone was taking license to the transistor technology, chef Julia Child was dealing with a surprising setback. When we think of disruptive innovations, we don’t think of chefs, but Child changed the world of cooking, making it much easier for people to cook great French dishes in their own homes. “Pull back and watch the full movie to understand disruptive change.” In 1951, the French chef failed her final exam at Le Cordon Bleu. That same year, she met Simca Beck and Louisette Berthold. The two were working on a book that would bring French recipes to an American audience. They asked Julia to join the team and bring her voice to the project. She agreed. Mastering the Art of French Cooking came out 10 years later. Success was not a straight line. There were three different publishers and one near-death experience in November 1959, in which, at the very last minute, publisher number two said this book cannot be published. This is predictable. Every story of disruptive innovation has twists and turns and fumbles and false steps and things that look and feel like failures. You cannot predict the specifics. You can, however, predict they will happen. What separates success from failure is not how good the original idea was. It’s how the disruptive innovator deals with the journey. When you’re trying to understand disruption, focus on patterns like this. Recognize that a single moment can deceive you. Pull back and watch the full movie to understand disruptive change. Julia Child ultimately passed her test at Le Cordon Bleu and, in my opinion, her chocolate mousse recipe is perfection. 4. Disruption casts a shadow. Disruption is very good for some, but it can be less good for others. Particularly in the middle of a disruptive change, there can be a lot of messiness. Back in the 1920s, Henry Ford was obsessed with his vision to create a car for the great multitude. In 1908, he rolled out the Model T. It cost $890, or about $30,000 in today’s terms. By 1924, the assembly line and lower employee turnover, facilitated by better wages, allowed Ford to dramatically decrease the cost to $260, or approximately $5,000 in today’s terms. Sales of automobiles took off. This was good for some, but less good for others. Cities were designed for people, not for cars. There were no traffic signals. There were no rules and norms governing who could do what, and sadly, people were getting hit, injured, and sometimes killed. Two sides broke out. The motorists said, “The problem here are the pedestrians. We’re going to brand them as jaywalkers.” Jay being slang for a country bumpkin who wasn’t very educated. They had Boy Scouts hand out cards in cities, telling people to cross at designated areas. “This was good for some, but less good for others.” The pedestrians fought back. They sought to brand the motorists as flivverboobs. Flivver was slang at the time for cars, and boob . . . well, that’s still pretty universal. You know who won the battle. In 1924, a New York traffic warden said, “We now know about 80 percent of incidents are caused by jaywalkers.” By the late 1920s, the word ‘flivverboob’ had basically disappeared. Disruption always casts a shadow. The middle can be very messy. You have to understand it, or it will swallow you. 5. Success with disruption requires patient perseverance. People talk about the accelerating pace of change, but we forget that when we see a big breakthrough, there’s often been decades of work before it. For example, in 2022 OpenAI introduced ChatGPT. It became the fastest technology in history to get to 100 million users. But by some dimensions, that technology was 67 years old, tracing back to a 1956 conference at Dartmouth College where the term ‘AI’ was coined. Around the same time as that conference, a chemist at Corning, Don Stookey, made a surprising discovery. He accidentally set his kiln to a temperature that was way too hot. He expected a gooey mess, but instead he discovered the first synthetic glass ceramic. Corning commercialized this in a line of kitchenware and, in parallel, launched Project Muscle to make the material clear. The result was something 14 times stronger than normal glass. But Corning couldn’t make it thin. They thought a possible market could be automobile windshields, but tests with crash test dummies showed that the head would not survive a collision with the glass because it was that strong. In 1971, after $300 million investment in today’s terms, Corning put the project on ice. In 2007, Steve Jobs was getting ready to launch the iPhone. He picked up the prototype, and its plastic screen just didn’t appeal to his aesthetic sense. He wanted glass. He knew Corning had provided an innovative screen for Motorola’s RAZR phone. Even though Corning shut down the project, they continued experimenting and exploring, and ultimately made the glass thinner. They called it Gorilla Glass. Steve Jobs came to Corning’s headquarters, talked to CEO Wendell Weeks, and said, “I want this, I want it at scale, and I want it fast.” Weeks said, “Great, but we can’t do it at scale and we can’t do it fast.” Steve Jobs turned on his reality distortion field and, without blinking, said, “Yes, you can. You can do it.” And Corning did. By 2024, eight billion devices had screens with Gorilla Glass. When it comes to disruption, you must be comfortable being uncomfortable because it almost always takes a lot longer than you think. This article originally appeared in Next Big Idea Club magazine and is reprinted with permission. View the full article

A majority of Gen Z workers are turning to AI chatbots during the workday for personal reasons, including mental health support, with 40% saying they talk to AI for at least an hour every day, according to a new Resume.org survey. “Many Gen Zers entered hybrid or remote jobs where casual mentorship or watercooler chats never formed, so AI fills that relational void,” said Kara Dennison, Resume.org’s head of career advising. “It listens, it responds thoughtfully, and it never criticizes.” She added: “That creates a sense of psychological safety that’s often missing in corporate hierarchies. It’s about connection, control, and immediacy. They’re using AI the way earlier generations used coffee breaks or hallway chats: to decompress, problem-solve, or feel understood.” While older generations might describe ChatGPT as a “tool,” 47% of Gen Z say it feels far more personal: 25% of Gen Z describe ChatGPT, Copilot, and other AI bots as their therapist or coach, a friend, or coworker, while 34% admit to confiding in AI chatbots about things they’ve never told another person. Some 16% say they frequently discuss personal topics such as mental health or relationships with AI, while 33% say they do so occasionally. Resume.org’s survey collected data from 1,000 full-time U.S. Gen Z workers ages 18 to 28 who used an AI chatbot such as ChatGPT or Copilot in the past week. Gen Z may be using ChatGPT for therapy, but mental health experts say it comes with risks. “Using a general-purpose chatbot as a therapist compromises the fundamental elements of safe care: clinical oversight, legal confidentiality, and a dependable route to human intervention,” Gijo Mathew, chief product officer at Spring Health, a global mental health platform for employers and health plans, told Fast Company. “This can introduce significant risks, particularly in multi-turn, emotionally charged discussions,” Mathew continued. “Most chatbots and large language models (LLMs) were not designed for mental health support and may overlook warning signs or offer articulate yet clinically unsound advice.” According to the survey, 43% of Gen Z workers spend at least 30 minutes per day using ChatGPT or a similar AI chatbot; 13% use it for one to two hours a day; 6% for two to four hours a day; and 5% for more than four hours a day. When it comes to dealing with stress and well-being on the job, 38% of Gen Z are turning to AI to take breaks, and 33% to talk through work-related stress or frustrations. That’s time that could be spent interacting with other humans. The findings also come at a time when 89% of corporate workers say they faced at least one mental health challenge in the past year. View the full article